Attention Enhancement With Parallel Groups for Remote Sensing Object Detection

Nowadays, remote sensing object detection has benefited a lot from the development of convolutional neural networks (CNNs). However, it is still a challenging task due to arbitrary orientation and dense distribution of objects in remote sensing images. To deal with these difficulties, we propose two effective attention mechanisms with parallel groups strategy to enhance feature representations in the detection head, named PGAE-head. Significantly, our designs can achieve competitive performance improvement by only introducing tiny parameters and computations in the model. Firstly, the features received by the PGAE-head are divided into multiple groups, which ensures the independence of each group during subsequent attention enhancement. Then, PGAE-head processes these sub-features with enhanced attention mechanisms based on spatial and channel dimensions in parallel to detect more accurate results. Experiments on DOTA and HRSC datasets show that the proposed PGAE-head achieves comparable performances with other state-of-the-art CNN-based models at minimal optimization costs, demonstrating its effectiveness.