TSW-YOLO-v8n: Optimization of Detection Algorithms for Surface Defects on Sawn Timber

Abstract

The goal of this work was to better meet the demand for rapid detection of surface defects in sawn timber in forestry production. This paper introduces a two-way feature fusion network based on the YOLO-v8 algorithm and proposes a feature fusion network model that combines the attention mechanism and loss function optimization. In this way it increases the tiny target detection head in order to more effectively detect small defective targets in the wood, thus realizing the model's high-efficiency and low-consumption functional design. The results show that the improved TSW-YOLO-v8n model realized the identification of eight kinds of defects in sawn timber with a high efficiency of 91.10% mAP50 and an average detection 6 ms, which is 5.1% higher than the original model’s mAP50 and 1 ms shorter than the original model’s average detection time. The comparison of the original model and its mainstream algorithms shows that the model of this paper had better performance and better detection capability. Thus, the improved model achieved better overall performance and stronger detection ability, which provides a new idea for the development of detection technology in the forestry industry.

Keywords

• deep learning
• target detection
• surface defects of sawn timber
• yolo-v8
• attention mechanism
• loss function
• feature fusion