C3-Light Lightweight Algorithm Optimization under YOLOv5 Framework for Apple-Picking Recognition

As the fruit-picking process is a labour-intensive and time-consuming task, the accurate and efficient recognition of apples during picking is of great significance for improving the overall efficiency of apple harvesting, reducing labour costs, and enhancing the quality of fruit picking. Although YOLOV5 algorithm can effectively detect apple status, its use on portable mobile devices still faces problems such as running lag. This paper is dedicated to the optimization of the C3-Light lightweight algorithm based on the widely used YOLOv5 framework for apple-picking recognition. The network structure of the C3-Light algorithm is redesigned. By introducing novel convolutional block arrangements and fine-tuning the hyperparameters related to the network layers, the model's feature extraction ability is enhanced while maintaining a relatively simple architecture. Through these improvements, the calls for hardware resources are remarkably reduced. Experimental results clearly demonstrate that the lightweight C3-Light model can maintain the original high-level accuracy. Specifically, it reduces GFLOPs by approximately 17% compared to the original model, which means a significant decrease in the computational complexity. Moreover, the GPU memory usage is decreased by 11%, indicating better memory utilization efficiency.