Complex Road Target Detection Algorithm Based on Improved YOLOv5
Computer Engineering and Applications
2022, 58 ( 17):
Aiming at the problem of false detection and missed detection caused by dense occluded targets and small targets in complex road background, a complex road target detection algorithm based on improved YOLOv5 is proposed. Firstly, Quality Focal Loss is introduced, which combines the classification score with the quality prediction of location to improve the positioning accuracy of dense occluded targets. Secondly, a shallow detection layer is added as the detection layer of smaller targets, the three-scale detection of the original algorithm is changed to four-scale detection, and the feature fusion part is also improved accordingly, which improves the learning ability of the algorithm to the features of small targets. Then, based on the feature fusion idea of weighted bidirectional feature pyramid network（BiFPN）, a de-weighted BiFPN is proposed, which makes full use of deep, shallow and original feature information, strengthens feature fusion, reduces the loss of feature information in the process of convolution, and improves the detection accuracy. Finally, the convolution block attention module（CBAM） is introduced to further improve the feature extraction ability of the algorithm and make the algorithm pay more attention to useful information. The experimental results show that the detection accuracy of the improved algorithm in this paper on the public autopilot data set KITTI and the self-made rider helmet data set Helmet reaches 94.9% and 96.8% respectively, which is 1.9 percentage points and 2.1 percentage points higher than the original algorithm, and the detection speed reaches 69 FPS and 68 FPS respectively. It has better detection accuracy and real-time performance. At the same time, compared with some mainstream target detection algorithms, the improved algorithm in this paper also has some advantages.
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