SCES: Surface condition evaluation system for electrical and electronic equipment reuse grading by combining defect detection and segmentation—A case study of laptops

Abstract

An essential step to maximize value recovery in Electrical and Electronic Equipment (EEE) is grading it based on surface defects for reuse. Although state-of-the-art computer vision-based methods can overcome manual inspections’ subjectivity, they are limited by different scales of defects and time-consuming segmentation annotation of small defects. This study addresses these challenges by proposing a Surface Condition Evaluation System (SCES). SCES refines manual grading guides for quantitative evaluation, enhances YOLOv8 with proposed Defect Channel and Spatial (DCS) attention modules and Slicing Aided Hyper Inference (SAHI) to improve defect detection, then cascades Segment Anything Model 2 (SAM2) with Zhang-Suen thinning algorithm to segment and measure small defects without segmentation annotations. SCES achieves the detection average precision (AP) of 98.9 %, 81.2 %, and 80.5 % for missing_battery, missing_cover, and scratch on the collected laptop surface defect dataset, respectively, which are higher than other state-of-the-art methods. Additionally, the measurement accuracy for scratches reaches over 85 %, verifying the reliability of the proposed method. The results indicate that SCES employs novel computer vision methods to realize the automated and quantified product surface condition grading. This research supports the transition from manual evaluation to an efficient and objective automated approach in the EEE reuse process, thereby contributing to resource conservation.