Abstract: To solve the problems of low efficiency and slow speed of Helicobacter pylori immunoblot images that heavily rely on visual recognition by physicians, an improved YOLOv5-based Helicobacter pylori immunoblot image detection model is proposed. Firstly, the feature extractor of YOLOv5 is optimized, and DenseNet is used as a new feature extractor to solve the problem of gradient disappearance. Then, by limiting the maximum downsampling multiple, the model is more sensitive to small target detection. Finally, the Swish activation function is introduced to replace the original YOLOv5 LeakyReLU activation function and improve IoU to obtain more accurate boundary information. The detection ability of the improved model is verified in the Helicobacter pylori immunoimprint image data set. The experimental results show that the F1-score of the improved model is as high as 0.93, mAP@0.5 up to 95.4% , mAP@0.5 : 0.95 up to 75.6%, and the detection frames per second is 54 fps, which can meet the clinical detection time limit requirements.

Key words: Helicobacter pylori western blot image, feature extraction, YOLOv5, target detection and recognition, deep learning