In real-time detection of saw chain defects based on machine vision, factors like oil contamination and dust impact image brightness and quality, leading to a decrease in the feature extraction capability of the object detection network. In this paper, an automated saw chain defect detection method that combines low-light enhancement and the YOLOv3 algorithm is proposed to ensure the accuracy of saw chain defect detection in complex environments. In the system, the RRDNet network is used to adaptively enhance the brightness of the saw chain image and restore the detailed features in the dark areas of the image. The improved YOLOv3 algorithm is used for defect detection. FPN structure is added with a feature output layer, the a priori bounding box parameters are re-clustered using the K-means clustering algorithm, and the GIoU loss function is introduced to improve the object defect detection accuracy. Experimental results demonstrate that this approach significantly improve image illumination and recover image details. The mAP value of the improved YOLOv3 algorithm is 92.88%, which is a 14% improvement over the original YOLOv3. The overall leakage rate of the system eventually reduces to 3.2%, and the over-detection rate also reduces to 9.1%. The method proposed in this paper enables online detection of saw chain defects in low-light scenarios and exhibits high detection accuracy for various defects.