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Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision (mAP) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection.