Fine grained dual level attention mechanisms with spacial context information fusion for object detection

For channel and spatial feature map C×W×H in object detection task, its information fusion usually relies on attention mechanism, that is, all C channels and the entire space W×H are all compressed respectively via average/max pooling, and then their attention weight masks are obtained based on correlation calculation. This coarse-grained global operation ignores the differences among multiple channels and diverse spatial regions, resulting in inaccurate attention weights. In addition, how to mine the contextual information in the space W×H is also a challenge for object recognition and localization. To this end, we propose a Fine-Grained Dual Level Attention Mechanism joint Spacial Context Information Fusion module for object detection (FGDLAM&SCIF). It is a cascaded structure, firstly, we subdivide the feature space W×H into n (optimized as n = 4 in experiments) subspaces and construct a global adaptive pooling and one-dimensional convolution algorithm to effectively extract the feature channel weights on each subspace respectively. Secondly, the C feature channels are divided into n (n = 4) sub-channels, and then a multi-scale module is constructed in the feature space W×H to mine context information. Finally, row and column coding is used to fuse them orthogonally to obtain enhanced features. This module is embeddable, which can be transplanted into any object detection network, such as YOLOv4/v5, PPYOLOE, YOLOX and MobileNet, ResNet as well. Experiments are conducted on the MS COCO 2017 and Pascal VOC 2007 datasets to verify its effectiveness and good portability.