Research on in-vehicle safety and security system based on face and back detection

ZHANG Chao; GU Tao

doi:10.19956/j.cnki.ncist.2024.03.008

Abstract：In order to solve the security risks caused by abnormal temperature inside the car,an in-vehicle safety and security system based on face and back shadow detection is proposed.It is realized by using Jetson nano hardware combined with OpenCV image processing and yolov5 target detection algorithm.The robust-ness of the model is improved by creating a dataset specifically applied to the in-vehicle scene.The yolov5 al-gorithm is improved for the car scene by using Kmeans++algorithm to generate anchor frames with better per-formance,optimizing the CBMA attention module to improve the algorithm's focus on face and back features,and the EIOU loss function to solve the localization problem of the predicted frames and the real frames in the case that the aspect ratio is the same and the centroids are coincident.Through experimental comparison,the accuracy of the improved algorithm increases by 4.6%,the recall increases by 1.3%,the mAP_0.5 increases by 8.8%,and the mAP_0.5:0.95 increases by 4.4%.The face and back shadow detection experiments show that the face and back shadow can still be detected correctly in the case of face and back shadow occlusion in the car.Through the real-time temperature monitoring in the car,combined with the face and back shadow de-tection model,the experiment shows that it is feasible to control the warning and window lifting and lowering by Jetson nano to protect the life safety.