Xiaomila Green Pepper Target Detection Method under Complex Environment Based on Improved YOLOv5s

Abstract

Real-time detection of fruit targets is a key technology of the Xiaomila green pepper (Capsicum frutescens L.) picking robot. The complex conditions of orchards make it difficult to achieve accurate detection. However, most of the existing deep learning network detection algorithms cannot effectively detect Xiaomila green pepper fruits covered by leaves, branches, and other fruits in natural scenes. As detailed in this paper, the Red, Green, Blue (RGB) images of Xiaomila green pepper in the green and mature stage were collected under natural light conditions for building the dataset and an improved YOLOv5s model (YOLOv5s-CFL) is proposed to improve the efficiency and adaptability of picking robots in the natural environment. First, the convolutional layer in the Cross Stage Partial (CSP) is replaced with GhostConv, the detection speed is improved through a lightweight structure, and the detection accuracy is enhanced by adding a Coordinate Attention (CA) layer and replacing Path Aggregation Network (PANet) in the neck with Bidirectional Feature Pyramid Network (BiFPN). In the experiment, the YOLOv5s-CFL model was used to detect the Xiaomila, and the detection results were analyzed and compared with those of the original YOLOv5s, YOLOv4-tiny, and YOLOv3-tiny models. With these improvements, the Mean Average Precision (mAP) of YOLOv5s-CFL is 1.1%, 6.8%, and 8.9% higher than original YOLOv5s, YOLOv4-tiny, and YOLOv3-tiny, respectively. Compared with the original YOLOv5 model, the model size is reduced from 14.4 MB to 13.8 MB, and the running speed is reduced from 15.8 to 13.9 Gflops. The experimental results indicate that the lightweight model improves the detection accuracy and has good real-time performance and application prospects in the field of picking robots.