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Dhaka, the capital of Bangladesh is the most densely populated city in the world and Traffic the ugliest side of Dhaka's development. The country is full of uneducated and undisciplined drivers who have no respect for the traffic rules and regulations. The tendency of breaking the law and overtaking of the drivers makes controlling the traffic hard...
Citations
... The luminosity method is a more sophisticated version of the average method. It also averages the values, but it forms a weighted average to account for human perception (Javed Mehedi Shamrat et al., 2020). The luminosity method is used to obtain the attribute colour value based on the assigned weights to each colour according to equation (1). ...
In India, approximately one-third of agricultural produce is wasted every year due to the different issues present in the post-harvest supply chain stages. The supply chain management of perishable products becomes complex and challenging due to the inclusion of its perishability dynamics. Quality is the main factor that governs the buying or discarding of perishable products by consumers. Therefore, the main aim of this research work is to develop an accurate and efficient image processing model for the classification of the product (Tomato) based on its quality for managing the supply chain. There are three novelties in this research work. A four-stage supply chain architecture integrated with the image processing system at mandi, and warehouses is proposed (First). This image processing system is developed in two stages. In stage I, the acquired images of tomato during its life cycle are labelled with the help of machine learning algorithms (Second). This labelled data is used in stage II for the development of a classification model to segregate the product into various grades. For this, an optimized architecture of seven-layer Convolutional Neural Network (CNN) model is developed followed by optimization of its hyperparameters simultaneously using Design of Experiments (DOE) technique (Third). The optimized CNN model achieved maximum accuracy of 88.40% and reported an execution time of 7 min. Further, the results of standard hyperparameter optimization techniques like Grid search, Random search, Bayesian, and Hyperband are compared with the proposed DOE technique on the optimized CNN architecture. The work done in this paper enables the supply chain managers to take accurate and rapid decisions for pricing, procurement, storage, and transportation at various stages of the supply chain leading to Industry 4.0. This will result in reduced post-harvest losses and simultaneously achieve the benefits across social, economic, and environmental dimensions of sustainability leading to better supply chain management.
... Table 1 summarize some of the studies conducted on improving traffic safety by mathematicians, engineers and city planners with a variety of results. Studies generally rely on different solution methods; every method has advantages and disadvantages [7][8][9][10][11][12]: Table 1. Summary of traffic detecting objects system/Model methods in previous studies ...
... Problem description Solution method Advantages Disadvantages [7] The first problem is people violating traffic regulations, which causes another problem, which is the traffic congestion. ...
Traffic safety aims to change the attitude of citizens towards careless traffic on the roads, making this the first step towards changing behavior. Also, teach the rules of safe pedestrian behavior and minimize the risks of road accidents. So many regulations have been set to avoid road accidents and traffic jams, which is the study scope of this paper using IT technology. With the expanding interests in Computer vision use cases such as vehicles self-driving, face recognition, intelligent transportation frameworks and so on individuals are hoping to assemble custom AI models to recognize and distinguish specific objects. Object detection is part of a computer's vision where objects that can be observed externally and are found in videos can be identified and tracked by computers. Therefore, object tracking is an important part of video analysis. There are many proposed methods such as Tracking, Learning, Detection, Mean shift and MIL. In this paper, the computer vision state in object detecting domain along with its challenges are discussed, also we address some requirements and techniques to overcome these challenges. Finally, TensorFlow technology is presented as a recommended solution to support Lane’s violation.
... Our system also informs drivers of traffic conditions via different channels in real-time. Shamrat et al. (2020) have developed a traffic detection system for Bangladesh and achieved an accuracy of 69% [26]. Chowdhury et al. (2018) have developed a system for counting the number of vehicles at road junctions. ...
... Our system also informs drivers of traffic conditions via different channels in real-time. Shamrat et al. (2020) have developed a traffic detection system for Bangladesh and achieved an accuracy of 69% [26]. Chowdhury et al. (2018) have developed a system for counting the number of vehicles at road junctions. ...
Mauritius faces traffic jams regularly which is counterproductive for the country. With an increase in the number of vehicles in recent years, the country faces heavy congestion at peak hours which leads to fuel and time wasting as well as accidents and environmental issues. To tackle this problem, we have proposed a system which consists of detecting and tracking vehicles. The system also informs users once a traffic jam has been detected using popular communication services such as SMS, WhatsApp, phone calls, and emails. For traffic jam detection, the time a vehicle is in the camera view is used. When several vehicles are present at a specified location for more than a specified number of seconds, a traffic jam is deemed to have occurred. The system has an average recognition accuracy of 93.3% and operates at an average of 14 frames per second. Experimental results show that the proposed system can accurately detect a traffic jam in real time. Once a traffic jam is detected, the system dispatches notifications immediately, and all the notifications are delivered within 15 s. Compared to more traditional methods of reporting traffic jams in Mauritius, our proposed system offers a more economical solution and can be scaled to the whole island.KeywordsVehicle detectionTraffic jam detectionTraffic notification
... However, the large number of sensors and data includes in the system cause to have high security measures for the system. A Smart automated system was developed to manage the traffic using Image Processing technology [17]. The system was implemented to control overtaking and law-breaking as it affects manage the road traffic. ...
With the development, almost all the sectors, countries tend to grow adapting to latest technologies. The transport sector also has a huge impact on this development sphere. When it comes to traffic, it is a huge problem in the world. In Sri Lanka traffic is a problem that exists for a long period. Annually there is a loss of Rs. 400 billion due to traffic congestions. Over the past years various solutions have been proposed using different methods for traffic control. These solutions are based on different trending technologies such as machine learning, image processing, fuzzy logic and the Internet of Things. However, those systems are not able to handle traffic congestions problems in complex environments. Therefore, a real-time traffic controlling system with the ability to handle traffic congestions in dynamic environment is highly valued. This study aims to design and build a real-time traffic management system using Multi Agent technology. Simulations for both existing system and Agent-Based system are implemented using NetLogo simulation tool and compared for various traffic situations. According to the results obtained from the two simulations, Agent-Based systems provide more accuracy and efficiency than the existing fixed scheduling systems.
... La falta de cultura ciudadana junto con los embotellamientos ocasiona que los conductores adopten conductas agresivas en la vía con el fin de llegar con premura a su punto de destino, generando accidentes que algunas veces resultan en lesiones importantes para ellos mismos o para terceros, y en los peores de los casos, pérdida de su vida. [3] El proyecto Visión Cero tiene como premisa que "la pérdida de la vida humana en tráfico es inaceptable". [2] Este proyecto fue lanzado en 1995 en Suecia y enél se adoptan medidas para reducir los accidentes que ocasionan lesiones graves o la muerte en la vía. ...
... Un método usado para la detección de vehículos es usando las características de Haar, el cual utiliza las intensidades lumínicas de cada pixel para generar un modelo simple en una región. Tal es el método usado en [3] y [11], es justo notar que las dos fuentes proponen el uso de sus algoritmos en sistemas embebidos, esto es porque la obtención de las características de Haar suponen poco esfuerzo en términos de procesamiento. Sin embargo, [11] propone una segunda etapa de procesamiento de la información en la nube usando Redes Neuronales Convolucionales CNN (por su sigla en inglés Convolutional Neural Networks) con el fin de corroborar la información obtenida en la detección usando las características de Haar. ...
Accidents on road in Colombia rates are growing, becoming a public health problem in the last decade. The accidents are often caused by reckless drivers, therefore several programs define safety on-road measures to mitigate deaths on roads. One of these measures is the speed control, a way to applying it is by impose traffic infractions to drivers who overpass speed limits. This state of the art presents research performed in regards of topics related to automation of this matter, specifically using computer vision. Obtaining the state of the art was done by getting documents related with the topic and filtering through a couple of stages taking into account different parameters. Detailed information in this matter can be seen in section II. Vehicle detection, road detection, speed estimation were identified as main works in this area. Also a couple of databases were identified for performing testing.
... It occurs after the unit testing phase. Purpose of integrated testing is to expose faults within the interaction between integrated units [28]. Table I shows test cases, expected results and observed results for each module of the system. ...
In SouthEast Asian cities such as Delhi, Dhaka road accidents are a very common occurrence which brings disaster to human lives as well as infrastructures. Sometimes people cannot reach hospitals prompt after an accident because of the traffic jam, deficit of ambulance, lack of a mechanism to timely propagate information to the appropriate authority. To ensure the safety of lives, this paper proposes an automated IoT based effective accident detection system. Immediately after an incident, the data information is sent to the webserver, instant SMS is forwarded to the victim's acquaintances and also to the relevant authorities such as traffic control room, nearby police station, ambulance service. To evaluate the performance of the system, a simulated road scenario has been designed. The result obtained after a thorough integration and system testing demonstrates that the proposed system not only achieves the stated goal of the research but also can deliver the expected outcome in a rather cost-effective way.
The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset's average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset's effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union (IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities.
Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients’ lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively.
