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To address the issues associated with the existing algorithms for the currentapple detection, this study proposes an improved YOLOv5s-based method, namedYOLOv5s-BC, for real-time apple detection, in which a series of modificationshave been introduced. Firstly, a coordinate attention (CA) block has beenincorporated into the backbone module to construct a new backbone network.Secondly, the original concatenation operation has been replaced with abidirectional feature pyramid network (BiFPN) in the neck module. Lastly, a newdetection head has been added to the head module, enabling the detection ofsmaller and more distant targets within the field of view of the robot. Theproposed YOLOv5s-BC model was compared to several target detection algorithms,including YOLOv5s, YOLOv4, YOLOv3, SSD, Faster R-CNN (ResNet50), and FasterR-CNN (VGG), with significant improvements of 4.6%, 3.6%, 20.48%, 23.22%,15.27%, and 15.59% in mAP, respectively. The detection accuracy of the proposedmodel is also greatly enhanced over the original YOLOv5s model. The modelboasts an average detection speed of 0.018 seconds per image, and the weightsize is only 16.7 Mb with 4.7 Mb smaller than that of YOLOv8s, meeting thereal-time requirements for the picking robot. Furthermore, according to theheat map, our proposed model can focus more on and learn the high-levelfeatures of the target apples, and recognize the smaller target apples betterthan the original YOLOv5s model. Then, in other apple orchard tests, the modelcan detect the pickable apples in real time and correctly, illustrating adecent generalization ability.

YOLOv5s-BC: An improved YOLOv5s-based method for real-time apple detection