Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, tr...
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At present, infrared intelligent detection technology has been widely used in various fields. Due to the development of its technology, how to interfere with important targets such as intelligent recognition of vehicl...
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Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, tr...
详细信息
Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can’t continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m*m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model’s detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. Keywords—Deep learning; Object detecti
At present, infrared intelligent detection technology has been widely used in various fields. Due to the development of its technology, how to interfere with important targets such as intelligent recognition of vehicl...
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ISBN:
(数字)9798350350760
ISBN:
(纸本)9798350350777
At present, infrared intelligent detection technology has been widely used in various fields. Due to the development of its technology, how to interfere with important targets such as intelligent recognition of vehicles has become a hot issue in the current technical field. Therefore, this study is based on the near-infrared band. We generate vehicle target confrontation samples to interfere with infrared detector devices. This paper proposes to first collect the single-band image of the protection target through the infrared UAV, and aligns it to align the position of the protection target in the single-band image to obtain the salient features of the protection target. Then, based on the loss function calculated by machine learning method, the adversarial samples of single-band images are generated by using the basic iterative method. The constraint function is used to jointly optimize the loss function, so that the attack of the adversarial samples on the single-band detection system of the infrared UAV can reach the target value, and the adversarial samples of the infrared image in this state are obtained. Then, it is arranged in multiple positions on the target vehicle to complete the interference to the infrared detection system. We first need to complete the simulation attack experiment in the digital domain, and then complete the confrontation with the infrared detector by using a special material that meets the requirements in the physical domain. The experimental results show that the attack success rate of this method has a certain effect. This paper has a certain reference for the future research of vehicle infrared reconnaissance and anti-reconnaissance.
In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields including computer vision, natural language processing, and healthcare. In particular, DL is experiencing an increasing deve...
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