Improved YOLOv7 object detection algorithm for multiple road targets
The perception of road environment is an important component of autonomous driving tasks.To overcome the difficulties in detecting small targets,inconsistent detection target sizes,and occlusion of detection targets in road environment perception,we propose a deep learning enhancement method to improve target detection performance.First,the Bottleneck-ELAN module is developed as the backbone to enhance the model's feature extraction capability.The Gather-and-Distribute(GD)mechanism are also employed to achieve cross-scale fusion between feature maps,addressing the issue of information loss during feature fusion across different scales.Then,a combination of the Complete-IoU(CIoU)and Normalized Wasserstein Distance(NWD)loss functions is employed to address the inconsistency in sensitivity to object displacement and the smoothness disparity present in the single IoU loss function.Our experiment shows the average accuracy of the improved model on the BDD100K dataset reaches 43.4％,3.1％higher than that of the original YOLOv7 algorithm.Moreover,the accuracy of small object detection improves even more markedly,up by 10％.
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