Using Improved YOLOv5 Model to Detect Volume for Logs in Log Farms

Abstract

In this paper, we propose a new computer vision model called SE-YOLOv5-SPD for counting the number of log ends in large wood piles in log farms. This task traditionally requires a lot of manpower and previous computer vision methods struggle to detect logs in low pixels and small objects in images. Our model is based on the YOLOv5 model and incorporates the Squeeze-and-Excitation Networks (SENet) attention module and SPD-Conv (Space-to-Depth Convolution) module to improve accuracy. We also compare the performance of the SE attention module and SPD-Conv module to the CBAM attention module and Focus module using the SE-YOLOv5-SPD model. Results show that the SE-YOLOv5-SPD model can achieve excellent results of 0.652 in mAP50:95, 0.912 in mAP50, 0.968 in Precision, and 0.864 in Recall in a low-resolution environment with interference, which is significantly better than other models. Our findings indicate that the SE-YOLOv5-SPD model is a promising solution for counting the number of log ends in wood piles.