SCA-YOLOv4: you only look once with squeeze-and-excitation, coordinate attention and adaptively spatial feature fusion

How to effectively and efficiently identify multi-scale objects is one of the key challenges in object detection. In order to make the classification and regression of single-stage object detector more accurate, an improved algorithm named you only look once with squeeze-and-excitation, coordinate attention and adaptively spatial feature fusion (SCA-YOLOv4) was proposed based on YOLOv4 algorithm. In this paper, firstly, by studying the different combination models of squeeze-and-excitation (SE) and coordinate attention (CA) module, the optimal detection performance model of SE and CA combination model was determined. On this basis, squeeze-and-excitation coordinate attention (SECA) combination model was embedded in the head module of YOLOv4 algorithm to highlight the useful information of the feature graph and suppress the irrelevant information. Finally, adaptively spatial feature fusion (ASFF) module was embedded between neck and head module to learn how to filter other levels of features in space, so as to retain only useful information in order to correctly integrate different levels of features. The average accuracy of the proposed model was 91.1% and 46.4% on PASCAL VOC2007 and MS COCO2017 datasets respectively, which was 1.9% and 2.9% higher than those of the original YOLOv4 algorithm. Therefore, experiments indicate that the proposed SCA-YOLOv4 model exhibits optimal performance on the PASCAL VOC and COCO datasets.