Yanqi Dong’s research while affiliated with Beijing Forestry University and other places

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Publications (17)


Multiscale feature fusion and enhancement in a transformer for the fine-grained visual classification of tree species
  • Article

May 2025

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6 Reads

Ecological Informatics

Yanqi Dong

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Zhibin Ma

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[...]

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Feixiang Chen

Image Classification of Tree Species in Relatives Based on Dual-Branch Vision Transformer
  • Article
  • Full-text available

December 2024

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14 Reads

Forests

Tree species in relatives refer to species belonging to the same genus with high morphological similarity and small botanical differences, making it difficult to perform classification and usually requiring manual identification by experts. To reduce labor costs and achieve accurate species identification, we conducted research on the image classification of tree species in relatives based on deep learning and proposed a dual-branch feature fusion Vision Transformer model. This model is designed with a dual-branch architecture and two effective blocks, a Residual Cross-Attention Transformer Block and a Multi-level Feature Fusion method, to enhance the influence of shallow network features on the final classification and enable the model to capture both overall image information and detailed features. Finally, we conducted ablation studies and comparative experiments to validate the effectiveness of the model, achieving an accuracy of 90% on the tree relatives dataset.

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MAFIKD: A Real-Time Pest Detection Method Based on Knowledge Distillation

October 2024

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14 Reads

IEEE Sensors Journal

The significant damage caused by pests to crops has always been a pressing issue in agricultural production. To address the problems of low recognition accuracy, weak feature extraction capability, and poor robustness of lightweight pest detection models, this study proposes a knowledge distillation algorithm based on multi-attention feature fusion and adaptive fine-grained feature imitation (MAFIKD). MAFIKD consists of two parts: multi-attention feature fusion (MA) and fine-grained feature imitation (FI). MAFIKD combines MA and FI to enhance the attention of the student to the key features of the teacher, establishing diversified knowledge such as feature correlation and sample correlation to alleviate the difficulty of knowledge transfer in pest detection models. We used a self-made pest dataset to evaluate the proposed algorithm. Experimental results show that after applying MAFIKD, YOLOv5-CSPDarknet achieved 85.7% mAP@0.5 and 76.12% mmAP, which are 3.13% and 4.56% higher than the baseline, respectively. To verify the actual inference speed of the model, this study developed a mobile application for pest detection based on Android, using the NCNN high-performance neural network forward computing framework to deploy the pest detection model offline to mobile terminals, and deployed the model on the server using the Nginx+uWSGI+Flask architecture to provide online and offline pest detection services. Experimental results show that after applying MAFIKD, YOLOv5-CSPDarknet achieved an average detection frame rate of 10.1 FPS on the HUAWEI Enjoy 20, and the model size was only 14.5 MB, meeting the real-time detection requirements for field pests.


Geographic location map of Sichuan Wolong National Nature Reserve.
NAMAttention module structural diagram.
Schematic diagram of C3_MNAM module.
The smallest closed box (green), the anchor box (pink), the target box(blue) and the connection of centroids (red), where the area of the concatenation is Su=wh+wgthgt−WiHi.
Change in training loss values.

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Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model

April 2024

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69 Reads

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7 Citations

Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP.


Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

April 2024

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127 Reads

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9 Citations

With the progressively deteriorating global ecological environment and the gradual escalation of human activities, the survival of wildlife has been severely impacted. Hence, a rapid, precise, and reliable method for detecting wildlife holds immense significance in safeguarding their existence and monitoring their status. However, due to the rare and concealed nature of wildlife activities, the existing wildlife detection methods face limitations in efficiently extracting features during real-time monitoring in complex forest environments. These models exhibit drawbacks such as slow speed and low accuracy. Therefore, we propose a novel real-time monitoring model called WL-YOLO, which is designed for lightweight wildlife detection in complex forest environments. This model is built upon the deep learning model YOLOv5s. In WL-YOLO, we introduce a novel and lightweight feature extraction module. This module is comprised of a deeply separable convolutional neural network integrated with compression and excitation modules in the backbone network. This design is aimed at reducing the number of model parameters and computational requirements, while simultaneously enhancing the feature representation of the network. Additionally, we introduced a CBAM attention mechanism to enhance the extraction of local key features, resulting in improved performance of WL-YOLO in the natural environment where wildlife has high concealment and complexity. This model achieved a mean accuracy (mAP) value of 97.25%, an F1-score value of 95.65%, and an accuracy value of 95.14%. These results demonstrated that this model outperforms the current mainstream deep learning models. Additionally, compared to the YOLOv5m base model, WL-YOLO reduces the number of parameters by 44.73% and shortens the detection time by 58%. This study offers technical support for detecting and protecting wildlife in intricate environments by introducing a highly efficient and advanced wildlife detection model.


Figure 7. The inference process for Amur tiger re-ID.
Figure 10. (a,b) are examples of the results of applying our proposed network for Amur tiger re-ID. The number above the image shows the similarity ranking result, and the green color shows correct re-ID.
Training and Testing dataset used in the experiment.
Performing ablation experiments on the ATRW test dataset to demonstrate the effectiveness of the IFPM.
Ablation experiments on ATRW test data set prove the progressiveness of CBAM module in the local branch.
A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification

April 2024

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39 Reads

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1 Citation

Animals

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.


Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes

September 2023

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125 Reads

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8 Citations

The vertical structure of forest ecosystems influences and reflects ecosystem functioning. Terrestrial laser scanning (TLS) enables the rapid acquisition of 3D forest information and subsequent reconstruction of the vertical structure, which provides new support for acquiring forest vertical structure information. We focused on artificial forest sample plots in the north-central of Nanning, Guangxi, China as the research area. Forest sample point cloud data were obtained through TLS. By accurately capturing the gradient information of the forest vertical structure, a classification boundary was delineated. A complex forest vertical structure segmentation method was proposed based on the Forest-PointNet model. This method comprehensively utilized the spatial and shape features of the point cloud. The study accurately segmented four types of vertical structure features in the forest sample location cloud data: ground, bushes, trunks, and leaves. With optimal training, the average classification accuracy reaches 90.98%. The results indicated that segmentation errors are mainly concentrated at the branch intersections of the canopy. Our model demonstrates significant advantages, including effective segmentation of vertical structures, strong generalization ability, and feature extraction capability.


Combining the Back Propagation Neural Network and Particle Swarm Optimization Algorithm for Lithological Mapping in North China

August 2023

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176 Reads

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1 Citation

Lithological mapping is a crucial tool for exploring minerals, reconstructing geological formations, and interpreting geological evolution. The study aimed to investigate the application of the back propagation neural network (BPNN) and particle swarm optimization (PSO) algorithm in lithological mapping. The study area is the Beiliutumiao map-sheet (No. K49E011021) in Inner Mongolia, China. This area was divided into two parts, with the left side used for training and the right side used for validation. Fifteen geological relevant factors, including geochemistry (1:200,000-scale) and geophysics (1:50,000-scale), were used as predictor variables. Taking one lithology as an example, the lithological binary mapping method was introduced in detail, and then the complete lithology was mapped. The model was compared with commonly used spatial data mining methods using the E-measure, S-measure, and Weighted F-measure values. In diorite testing, the accuracy and kappa of the optimized model were 92.11% and 0.81, respectively. The validation results showed that our method outperformed the traditional BPNN and weights-of-evidence approaches. In the extension of the complete lithological mapping, the accuracy, recall, and F1-score were 82.66%, 74.54%, and 0.76, respectively. Thus, the proposed method is useful for predicting the distribution of one lithology and completing the whole lithological mapping at a fine scale. In addition, the trained network can be extended to an adjacent area with similar lithological features.


Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN

April 2022

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409 Reads

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32 Citations

Forests

Terrestrial laser scanning (TLS) can provide accurate and detailed three-dimensional (3D) structure information of the forest understory. Segmenting individual trees from disordered, discrete, and high-density TLS point clouds is the premise for obtaining accurate individual tree structure parameters of forest understory, pest control and fine modeling. In this study, we propose a bottom-up method to segment individual trees from TLS forest data based on density-based spatial clustering of applications with noise (DBSCAN). In addition, we also improve the DBSCAN based on the distance distribution matrix (DDM) to automatically and adaptively determine the optimal cluster number and the corresponding input parameters. Firstly, the proposed method is based on the improved DBSCAN to detect the trunks and obtain the initial clustering results. Then, the Hough circle fitting method is used to modify the trunk detection results. Finally, individual tree segmentation is realized based on regional growth layer-by-layer clustering. In this paper, we use TLS multi-station scanning data from Chinese artificial forest and German mixed forest, and then evaluate the efficiency of the method from three aspects: overall segmentation, trunk detection and small tree segmentation. Furthermore, the proposed method is compared with three existing individual tree segmentation methods. The results show that the total recall, precision, and F1-score of the proposed method are 90.84%, 95.38% and 0.93, respectively. Compared with traditional DBSCAN, recall, accuracy and F1-score are increased by 6.96%, 4.14% and 0.06, respectively. The individual tree segmentation result of the proposed method is comparable to those of the existing methods, and the optimal parameters can be automatically extracted and the small trees under tall trees can be accurately segmented.


Unsupervised Semantic Segmenting TLS Data of Individual Tree Based on Smoothness Constraint Using Open-Source Datasets

January 2022

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82 Reads

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4 Citations

IEEE Transactions on Geoscience and Remote Sensing

Unsupervised segmentation of Terrestrial Laser Scanning (TLS) data into wood and leaf is the key for studying forest carbon storage, photosynthesis, canopy radiation. Further segmentation of wood data into trunk and larger branch (TLB), remaining branch (RB) is of great significance and challenge for dust retention, soil heavy metal enrichment. We proposed an unsupervised, automatic semantic segmentation method based on TLS data of individual tree. The method firstly performs initial segmentation based on plane fitting residuals and neighborhood normal angle, which can extract smooth and connected regions in point cloud. Then, the geometric features of segmented clusters are quantified to approximate RB or leaf features. Finally, the segmentation of TLB, RB, and leaf is realized by combining different clusters from bottom to top with geometric features and neighborhood relations. The segmentation performance of our method was evaluated with 104 tree samples from 23 tree species in two open-source datasets from Indonesia, Peru, Guyana and from Canada and Finland. The micro-average precision of our method is 93.61%. The micro-average recalls of TLB, RB, and leaf are 97.08%, 86.44%, and 89.62%. Compared with the well-known method of separating wood and leaf, our method has 33.56% higher sensitivity, 1.82% higher specificity, 20.52% higher precision, and 0.217 higher F1-score. Besides, we estimated the surface area and volume of TLB, the surface area and volume of RB based on the segmented data. The above parameters have good consistency compared to those calculated based on manually separated point clouds (Pearson correlation coefficient (PCC) of 0.55-0.93).


Citations (13)


... In cultural heritage, YOLO is employed to detect and classify artifacts in archaeological studies, aiding in the preservation of cultural history and restoration of structural defects in heritage sites [43,44]. In environmental monitoring, YOLO assists in tracking endangered species and identifying deforestation patterns through satellite imagery [45]. In healthcare, YOLO excels in automating critical tasks, such as detecting tumors in medical imaging and monitoring surgical tools during procedures [46]. ...

Reference:

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection
Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model

... Despite advancements in YOLO's architecture, newer versions still face challenges in accurately detecting animals in complex natural environments. Issues such as weather variations, lighting changes and animal occlusions frequently result in missed detections [29]. To overcome this, the model needs to capture richer features. ...

Wildlife Real-Time Detection in Complex Forest Scenes Based on YOLOv5s Deep Learning Network

... PointNet, initially introduced by the researcher Charles R. Qi in 2017, is a robust deep-learning network specializing in processing point cloud data [12]. The network distinguishes itself from other deep learning networks by efficiently mitigating the impact of the point cloud data's disorganized and inflexible rotational properties on classification accuracy [17,[19][20][21]. Since its proposal, several different network versions have been created and utilized to classify point clouds. ...

Forest-PointNet: A Deep Learning Model for Vertical Structure Segmentation in Complex Forest Scenes

... Traditional lithological mapping relies heavily on manual field surveys, and the accuracy of lithological mapping is significantly constrained by the expertise of mappers. Typically, different individuals may produce different results of lithological mapping and this process is commonly imprecise, slow and costly (Dong et al. 2023). Therefore, the urgent challenge in the field of geology is how to realize accurate and automatic lithological mapping by using various geological survey data which can be directly acquired from the field with specific equipment or from the laboratory. ...

Combining the Back Propagation Neural Network and Particle Swarm Optimization Algorithm for Lithological Mapping in North China

... Conventional machine vision techniques rely on image processing and geometry-based algorithms to analyze and interpret data derived from the natural environment. Leveraging common geometric features (curvature, density, etc.), growing patterns, and topological information [11,12], conventional machine vision techniques facilitate both wood-leaf separation and structural continuity analysis of arboreal elements [13]. Moreover, identifying non-photosynthetic constituents based on segment linearity, along with other methods, enhances the analysis, further augmenting the overall analytical process [14]. ...

Unsupervised Semantic Segmenting TLS Data of Individual Tree Based on Smoothness Constraint Using Open-Source Datasets
  • Citing Article
  • January 2022

IEEE Transactions on Geoscience and Remote Sensing

... Different from airborne laser scanning (ALS), the terrestrial laser scanner (TLS) provides more accurate structural information about the understory. Fu et al. (2022) [22] detected the trunks using improved DBSCAN in TLS data and used Hough circle fitting to modify the detection results. Xu et al. (2023) [23] used Topology-based Tree Segmentation (TTS) to segment individual trees. ...

Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN

Forests

... The QSM creation relies on fitting 3D cylinders into the point cloud, trying to copy the structure of a tree as well as possible while coping with noise and gaps in the point cloud [23]. Alternatives to the TreeQSM method include the SimpleTree [24] and AdQSM [25] programs, which claim to incorporate certain improvements over TreeQSM. All these software tools employ similar Quantitative Structure Modelling algorithms; however, TreeQSM is frequently used as a benchmarking method, and its code appears to be actively maintained. ...

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Forests

... A comprehensive evaluation was then conducted of three different open-source tree reconstruction tools (AdTree [16], TreeQSM [19,40], and Raycloudtools [36]). These tools are commonly used to reconstruct tree models from laser-scanned (LiDAR) point clouds with demonstrated accuracy ( [16,36,41]) and represent an evolution in tree reconstruction algorithms. Each tool employs a different approach for producing the initial tree skeletons, which form the basis of the 3D triangulated meshes. ...

AdQSM: A new method for estimating above-ground biomass from TLS point clouds

... Suárez et al. [29] described an approach based on aerial photography and airborne LiDAR to estimate individual tree heights in forest stands using a tree canopy model.Chen et al. [30] proposed the tree top position identification method based on the local maximum algorithm of the point cloud and used the watershed algorithm and template-matching algorithm to locate the trees and extract the tree height and crown width. Fan et al. [31] proposed a quantitative structural model based on the modified AdTree method, and the model was reconstructed to extract the tree volume, diameter at breast height (DBH), and tree height. However, these approaches have limitations in automatically distinguishing individual crown sizes, and further work is needed to estimate diameter distribution and volume [32]. ...

A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds

... Literature (De Lima et al., 2019) successfully classified microfossils, core images, rock micrographs, and hand sample images of rocks and minerals using a light-weight mobilenetv2 model, achieving an accuracy rate of 98%. Literature (Fan et al., 2020) established a rock image recognition model based on the lightweight network architecture ShuffleNet, combined with transfer learning methods. The recognition model achieved an accuracy of 97.65% on the PC testing dataset and 95.30% on the smartphone testing dataset. ...

A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

Mobile Information Systems