The application of adhesive on the white body is a crucial part of automotive production, and achieving automatic detection of adhesive defects on the white body is of great significance for improving the quality and efficiency of automotive production. However, based on traditional image processing and deep learning, current visual detection methods cannot effectively detect adhesive defects. Therefore, we propose a white body adhesive defect detection method combining deep learning and traditional image processing. First, Faster R-CNN was used to locate and extract adhesive strips, and the presence of adhesive breakage defects was determined based on the number of regions of interest. Then, the pixel area, length, and width of the adhesive strip were calculated using breadth-first search and skeleton extraction algorithms. Finally, the mapping ratio of actual width to pixel width was obtained through camera calibration, and the actual width of the adhesive strip was evaluated to determine whether the adhesive width was qualified and achieved defect detection of the adhesive. The validation experiment was conducted using a (10 ± 1) mm adhesive coating, and the results demonstrate that this method can accurately identify adhesive breakage defects. The measurement error of the width of the adhesive strip is found to be within ±0.35 mm. Furthermore, the detection speed is approximately 19.50 frame·s^-1, which meets the requirements of actual production.