A lightweight improved YOLOv5s model and its deployment for detecting pitaya fruits in daytime and nighttime light-supplement environments

Precise detection and low-cost deployment are the technological basis of intelligent fruit picking. This study proposes a lightweight improved YOLOv5s model to detect pitaya fruits in daytime and nighttime lightsupplement environments, and make it successfully deploy in an Android device. This model first uses the module of shufflenetv2 to reconstruct the YOLOv5s backbone network. Then, the study proposes a ConcentratedComprehensive Convolution Receptive Field Enhancement (C3RFE) module to improve the detection precision of pitaya fruits. Furthermore, a Bidirectional Feature Pyramid Network (BiFPN) feature fusion method is used to enhance the multi-scale feature fusion. Moreover, three optimized Squeeze-and-Excitation (SE) attention modules are added to make full use of the image feature information. Finally, a dynamic label allocation strategy simple Optimal Transport Assignment (simOTA) is used to optimize the YOLOv5s model original label allocation strategy. The experimental results show that the improved model achieves an average precision rate of 97.80 %, with frames per second (FPS) of 139 FPS in a GPU run environment. The model size is only 2.5 MB. Compared to the state-of-the-art SSD, Faster RCNN, YOLOv4, YOLOv4 tiny, YOLOv5s, YOLOv5Lite-s, YOLOXs, YOLOv7, YOLOv7-tiny, YOLOv8n and YOLOv8s, the improved YOLOv5s achieve preferred comprehensive performance in average precision rate, FPS and model size. When deploying this model on the Realme GT Android mobile phone by developing an application based on an NCNN framework, such an application accomplishes real-time pitaya fruit detection with an FPS exceeding 30 FPS. This study can provide technological support for a precise and effective pitaya fruit intelligent picking.