D. Rasi’s research while affiliated with Sri Krishna College of Engineering and Technology and other places

Across the globe, people are working to build "smart cities" that will employ technology to make people's lives better and safer. Installing cameras at strategic spots across the city to monitor public spaces besides provide real-time footage to law enforcement besides other local authorities is a crucial part of smart city infrastructure, which includes video surveillance. A more effective answer is provided by deep learning algorithms, however research in this area still faces significant problems from changes in target size, form change, occlusion, and illumination circumstances as seen from the drone's perspective. In light of the aforementioned issues, this study presents a highly effective and resilient approach for aerial picture identification. To begin, the concept of Bi-PAN-FPN is presented to enhance the neck component of YOLOv8-s, taking into consideration the prevalent issue of small targets being easily misdetected or ignored in aerial photos. We achieve a more advanced and thorough feature fusion procedure much as feasible by completely considering and reusing multiscale features. To further reduce the amount of parameters in the model and prevent info loss during long-distance feature transfer, the benchmark model's backbone incorporates the GhostblockV2 structure in lieu of a portion of the C2f module. With the help of the Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA), the suggested model's hyper-parameters are optimised. Lastly, a dynamic nonmonotonic focusing mechanism is employed in conjunction with WiseIoU loss as bounding box regression loss. The detector accounts for varying anchor box quality by utilizing "outlier" evaluations, thus improving the complete presentation of the detection task.

The first stage is to extract fine details from a picture using Red Green Blue (RGB) colour space is colour image segmentation. Most grayscale and colour picture segmentation algorithms use original or updated fuzzy c-means (FCM) clustering. However, due to two factors, the majority of these methods are inefficient and fail to produce the acceptable segmentation results for colour photos. The inclusion of local spatial information often results in a high level of computational complexity due to the repetitive distance computation between clustering centres and pixels within a tiny adjacent window. The second reason is that a typical neighbouring window tends to mess up the local spatial structure of images. Color picture segmentation has been improved by introducing Deep Convolution Neural Networks (CNNs) for object detection, classification and semantic segmentation. This study seeks to build a light-weight for object detector that uses a depth and colour image from a publically available dataset to identify objects in a scene. It's likely to output in the depth way by expanding the YOLO network's network architecture. Using Taylor based Cat Salp Swarm algorithm (TCSSA), the weight of the suggested model is modified to improve the accuracy of region extraction findings. It is possible to test the detector's efficacy by comparing it to various datasets. Testing showed that the suggested model is capable of segmenting input into multiple metrics using bounding boxes. The results shows that the proposed model achieved 0.20 of Global Consistency Error (GCE) and 1.85 of Variation of Information (VOI) on BSDS500 dataset, where existing techniques achieved nearly 1.96 to 1.86 of VOI and 0.25 to 0.22 of GCE for the same dataset.

Image segmentation is one of the most significant tasks in image analysis, and it plays an imperative job in image processing to analyse and attain meaningful information. Moreover, image segmentation is a major process of object recognition and categorization in computer vision domain. Image segmentation utilizes the image features for separating images into definite areas along with exclusive properties. Meanwhile, various colour image segmentation techniques are introduced in computer vision research area. However, these techniques are more time consuming and failed to afford anticipated segmentation outcome, because of poor segmentation results and high computational difficulty. To overcome these challenges, an effectual hybrid optimization-based Deep Learning (DL) technique is devised for colour image segmentation and classification in this research study. The median filter is applied for input image to eliminate the noises, which assists for better image segmentation and classification process. Moreover, Improved Invasive Weed Flower Pollination Optimization (IIWFPO) approach is introduced for image segmentation process in this work. In addition, Deep Residual Network (DRN) classifier is employed for image classification, and the classifier is trained by developed Fr-IIWFPO algorithm. The developed colour image segmentation and classification approach obtained better performance than traditional techniques with accuracy of 0.9187, sensitivity of 0.9334, and specificity of 0.8902.

Image classification becomes a popular research area in computer vision due to the increasing development of image indexing and retrieval tasks. This paper proposes a Flower Henry Gas Solubility Optimization-based Deep Convolution Neural Network (FHGSO-based Deep CNN) for image classification. Initially, the input image is pre-processed through the median filter. Then, the segmentation is performed using the Improved Invasive Weed Flower Pollination Optimization (IIWFPO)-based SegNet. IIWFPO is the integration of the Improved invasive weed optimization (IWO) algorithm and Flower Pollination Algorithm (FPA). Finally, image classification is performed using FHGSO-based Deep CNN. The FHGSO algorithm is developed by integrating the FPA and Henry Gas Solubility Optimization (HGSO) algorithm. The performance of the proposed method is analyzed using the Stanford background dataset and compared with the other image classification methods. The proposed model obtained the value of 0.938, 0.955, and 0.907 for testing accuracy, sensitivity, and specificity, respectively.