Addressing issues such as surface geometric distortion, high reflection, and the challenge of detecting minor defects on the external surfaces of stainless steel pots, this paper presents a novel approach for detecting defects on the external surfaces of cylindrical stainless steel pots based on high-resolution line-scan imaging. The method begins by creating a real-time inspection system that includes a line-scan camera and a strip light source. This system achieves distortion-free, high-resolution image acquisition of the outer surfaces of stainless steel cylindrical pots by establishing linear constraints on pot size, rotary table rotation speed, and line-scan frame rate. Subsequently, a You Only Look Once and fully convolutional network cascade neural network surface defect detection strategy is introduced, utilizing dual-channel images of the original and enhanced images as inputs. This approach enables the characterization of subtle defects in high-resolution image data. Finally, we conducted experiments using the proposed method on the provided dataset, and the results demonstrate the effectiveness of this approach in detecting various types of product defects. The mean pixel accuracy achieved a remarkable 91.69%, while the mean intersection over union score reached an impressive 83.59%. These findings provide an effective technical means for the qualitative detection of the types of defects on the surface of stainless steel pots and the quantitative measurement of the size of the defects.