Abstract
In order to solve the problem of low recognition rate and low real-time performance of vehicle detection in complex road environment, a data-driven forward vehicle detection algorithm based on improved tiny-YOLOv3 is proposed. Based on tiny-YOLOv3, the context feature information is combined to increase the two scale detections of tiny-YOLOv3 to three. The spatial pyramid pooling (SPP) module is added to increase the number of feature channels to improve the network feature extraction ability. According to the dense arrangement of vehicles on the horizontal axis in the road image ahead, we change the grid size of tiny-YOLOv3 and increase the number of candidate boxes on the horizontal axis. In addition, combined with the characteristics of the vehicle size in the road image ahead, K-means clustering method is used to select the appropriate number and size of target candidate boxes. We obtain the optimal detection model by multi-scale training of the improved network. The experimental results show that the average accuracy of the improved algorithm on the KITTI datasets is 91.03%, which is 7.12% higher than that of tiny-YOLOv3. And the detection speed of improved network is 144 frames/s, which meets the real-time requirements.
Highlights
Intelligent vehicle will be the inevitable trend of the future development of automobile industry
The results show that the Mean Intersection over Union (MIOU) of PPt-YOLOv3 is 5.24% higher than that of YOLOv3 and 8.38% higher than that of tiny-YOLOv3
The reason is that through K-means clustering analysis of data sets to select the appropriate size of the candidate box and improve the grid size, we can better improve the positioning accuracy of the model
Summary
Intelligent vehicle will be the inevitable trend of the future development of automobile industry. 106 layers, which has better feature extraction effect and improves the positioning and classification accuracy of target detection. SPP-net completes multi-level feature extraction through spatial pyramid pooling, enhances the robustness of the network and improves the detection accuracy and speed.
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