Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled ...Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model’s detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network’s average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.展开更多
基金Funded by Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,China(No.GZKF-202219)。
文摘Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model’s detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network’s average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.
