Automatic detection of tiny defects on coating surfaces holds significant importance in manufacturing quality control. Addressing the critical need for quality inspection of coating surfaces on large-scale aircraft panels, this paper proposes a machine vision-based multi-scale defect collaborative detection method, focusing on solving the challenge of synchronously identifying cross-scale defects including millimeter-scale pits, fisheyes, and bubbles as well as centimeter-scale scratches and sags. This paper first systematically analyzed the shortcomings of existing detection methods in terms of synchronous recognition of multi-scale defects and balancing real-time performance with high precision, and conducted research accordingly. The core contributions completed include: constructed a multi-light source collaborative image acquisition platform and established a coating image dataset covering five typical defect types; designed a composite image preprocessing method that integrates nonlinear illumination correction and multi-scale enhancement, effectively addressing brightness gradient anomalies caused by local reflections and sudden brightness changes; and proposed an improved GCSS-YOLOv8 deep learning model to enhance detection performance for defects of different scales. Experimental results show that the proposed method achieves a mean Average Precision (mAP@0.5) of 91.4% on the self-built dataset, which is 1.9% higher than the original YOLOv8 model, verifying its effectiveness and practical value.