Small Target Detection Algorithm for Printing Defects Detection Based on Context Structure Perception and Multi-Scale Feature Fusion

Small target detection is an important research direction in the field of computer vision, which is widely used in popular fields such as industrial defect detection, satellite remote sensing image detection. However, in printing defects detection, due to the complex defect background and small target, it is difficult to extract multi-scale features, and the extracted features have less available information. Therefore, this paper improves YOLOv7 and proposes an end-to-end printing defects detection algorithm based on context structure perception and multi-scale feature fusion (CM-YOLOv7). CM-YOLOv7 is mainly composed of a Context Structure Awareness Module (CSAM), a Multi-scale Feature Interaction Module (MFIM) and a Feature Refinement Layer Module (FRLM). Firstly, the CSAM uses multiple convolution kernels of different sizes to obtain features of different receptive fields and enhance multi-scale feature extraction. Secondly, for the extracted multi-scale features, the MFIM adaptively fuses the features of adjacent layers to achieve mutual learning between coarse-grained information and fine-grained information and improves the expression ability of small target features lost after convolution. Finally, in order to refine the edge information of the defect target more effectively and enhance the feature expression ability of the image, an FRLM is designed in MFIM. In the experimental part, this paper utilizes the printing defects detection dataset and DOTA-V1.0 dataset in order to better evaluate the effect of the algorithm. The experimental results show that the CM-YOLOv7 model proposed in this paper has a more accurate detection effect on the small target of printing defects.