Detection method of garden spherical hedge based on YOLOv5s

Under complex garden environment such as aggregation and occlusion, it is difficult for the existing target detection algorithms to accurately detect spherical hedge. In order to solve this problem, an algorithm YOLO-CBS based on YOLOv5s is proposed to improve the detection accuracy of spherical hedge in gardens. Firstly, coordinate attention（CA） is introduced into the backbone network of YOLOv5s, which considers the relationship between channels and the location information of the feature space, so that the model can more accurately identify and locate the target hedge. Secondly, the path aggregation network（PANet） is replaced by bidirectional feature pyramid network（BiFPN） to improve the efficiency of feature fusion. Finally, the non-maximum suppression（NMS） at the output is changed to Soft-NMS to improve the detection accuracy of target hedges under complex scenes such as occluded hedges and dense hedges. The results of experiments on a typical hedgerow data set show that the average accuracy of the YOLO-CBS algorithm is improved by 3. 4% compared to the YOLOv5s algorithm.