适用于鱼眼图像的改进 YOLOv7 目标检测算法

Images taken by fisheye cameras are characterized by wide field of view, geometric distortion and large scale variance, which bring great challenges to object detectors based on general convolutional networks. Existing object detection algorithms can be further improved with respect to network structure design, feature learning to be applicable to the distorted object detection task on fisheye images. To mitigate the effect of radial distortion on fisheye images, a multi-head attention module with multi-branch stacking structure is used in the YOLOv7 backbone to capture global contextual information. Meanwhile, a simple and efficient layer aggregation structure combining deformable convolutions is used on the Neck side of YOLOv7 to achieve effective multi-scale feature fusion. Experiments are conducted on the public comprehensive fisheye image dataset VOC_360, and the results show that the improved YOLOv7 fisheye image object detector effectively achieves detection accuracy of 84.3% and 70.4% for mAP50 and mAP50:95, respectively, which is 3.1 percentage points and 6.4 percentage points higher than the baseline model YOLOv7, respectively.