Intelligent Detection of Vehicle Driving Safety Based on Deep Learning

Abstract

With the continuous increase in the number of cars, traffic safety problems are also becoming more and more serious, whether the driver wears a seat belt to protect the driver's personal safety so that the problem can be solved in the event of a traffic accident. The author puts forward the research status of deep learning and convolutional neural network, as well as its theory and technology, and conducts in-depth analysis and research, a small target detection algorithm Deconv-SSD based on transposed convolution is proposed, driver area localization algorithm Squeeze-YOLO is based on lightweight model, and driver seat belt detection algorithm is based on semantic segmentation. Deconv-SSD achieves fast vehicle detection through depthwise separable convolution and fusion of multiresolution feature maps and then utilizes the salient features of the front windshield; through the method of lightweight feature extraction and the Squeeze-YOLO algorithm, the rapid positioning of the driver area is realized. Fast segmentation of seat belts is based on semantic segmentation algorithm and pruning technology in the positioning area, and by judging the maximum connected domain area after segmentation, the driver's seat belt detection is realized. Experiments and data analysis are carried out on the proposed algorithm. When the image resolution is consistent with the feature extraction model, the average accuracy of Deconv-SSD is compared with the original SSD algorithm in the PASCALVOC public dataset, from 77.2% to 79.6%. In the self-made seat belt detection dataset, Squeeze-YOLO can reach 73 FPS when the average accuracy is 99.96%, the semantic segmentation algorithm accelerated by pruning achieves an accuracy of 94.87% at a speed of 305 FPS, and the validity of the experiment is verified.