关键词: 板栗栗蓬, YOLOv3, 目标检测, CBAM, 焦点损失

Abstract: The artificial harvesting of Chinese chestnut is low efficiency, high labor intensity and easy to hurt people. Chestnut positioning and picking by unmanned aerial vehicle (UAV) based on machine vision are both efficient and safe. To quickly identify and accurately locate chestnut targets in natural environment, a YOLOvc chestnut target detection method with improved YOLOv3 network structure is proposed. By adding the CBAM attention mechanism module integrating channel attention and spatial attention mechanism to the front end of the network YOLOv3 layer, the extracting little target features ability of deep learning network model is improved. Secondly, the focal loss function is added on the basis of the original loss function of YOLOv3 to improve the detection and identification ability of difficult samples such as chestnut occlusion. The results show that YOLOvc algorithm can effectively detect chestnut, its accuracy rate and average accuracy are 89.35% and 89.37% respectively.The results of the ablation experiments showed that the precision of improved YOLOv3 convolutional neural network was 2.21% higher than the YOLOv3. The research results show that the deep learning algorithm YOLOvc by adding attention mechanism and focus loss function to YOLOv3 can effectively realize chestnut detection and localization on trees and provide effective technical support for chestnut harvesting by UAV. 

Key words: chestnut peng, YOLOv3, object detection, CBAM, focal loss