Detection method of Chinese chestnut in natural environment based on improved YOLOv8
In view of the high cost and safety risk caused by artificial knock harvest chestnut,it is very important to study the unmanned aerial vehicle chestnut harvest methods.In order to rapidly and precisely identify chestnut targets under natural light conditions,a modified convolutional network model detection method based on YOLOv8 was proposed.The CBAM attention mechanism was added to the C2f module of the YOLOv8 backbone network to enhance the convolutional network model ability of extracting chestnut features.A small chestnut target detection head was added to the head of YOLOv8 which formed the detection module together with the original three detection heads of YOLOv8.This method(YOLOv8-Vcj)enabled the network model to better capture the target features of small chestnut.Through training and validation experiments on the self-built data set,the detection accuracy of YOLOv8-Vcj was 1.3％higher than YOLOv8 and the mAP@0.5 and mAP@0.5∶0.95 values were 4.6％and 3.4％higher than YOLOv8,respectively.The chestnut detection error of the improved convolution network mainly comes from the light conditions and the density of chestnut targets in the images.The research results show that the improved convolutional neural network YOLOv8-Vcj of combining the CBAM attention mechanism and a small target detection head can effectively detect chestnuts on the tree.
chestnut pengYOLOv8object detectionCBAMdetection head
万方数据
维普
