This paper focuses on the comprehensive detection of defects in cold rolled steel through examination for surface and internal defects. Regarding surface defect detection, a bilateral line light illumination scheme is proposed and a comparison with line light illumination scheme is carried out. As for internal defect detection, the applicability of various metal internal inspection technologies such as X-ray, ultrasound, and infrared thermography is analyzed from the perspectives of detection resolution and defect edge characteristics. The results show that bilateral line light illumination not only increases the overall average precision of the YOLOv5 object detection algorithm model to 90.16% (an increase of 15.46% compared to the line light illumination) but also improves model classification and training efficiency. X-ray and ultrasound inspection technologies can detect blind holes with a diameter of 0.25 mm, while infrared thermography can detect blind holes with a diameter of 1 mm. In evaluating defect edge characteristics, X-ray inspection technology exhibits a minimum blind hole edge grayscale difference of 145, ultrasound of 89, and infrared thermography of 30. This study proposes an improved scheme for the detection of surface defects in cold rolled steel and offers insights for the research on internal defect detection.