Design of Border Suspicious Person Detection System Based on YOLOv5
Aiming at the fact that the traditional border monitoring system needs to rely on a large number of border guards,and the manpower is unclear in the visible light detection,an Android infrared detection system based on the YOLOv5s target detec-tion algorithm is proposed,which can run without a network.Taking the infrared data images captured by drones as samples,simu-lating the intrusion of suspicious persons in different scenarios at the border,and using deep learning algorithms for training,the de-tection accuracy in different scenarios can reach about 95%.Through ONNX model exchange format conversion,the NCNN model is easy to be deployed on the terminal,and Android Studio is used to deploy the model on the Android side,and finally the infrared im-age detection on the mobile phone side is realized.The experimental results show that the detection time of the infrared detection model deployed on the mobile phone for a single frame of suspicious persons around the border is less than 200 ms,and the detec-tion confidence reaches about 94%,which basically meets the expected goals of the system.
deep learningYOLOv5sIR detectionmodel conversionAndroid
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