Tiny Object Detection via Regional Cross Self-Attention Network

As vision sensor technology continues to evolve, the requirements for detecting targets of interest in the images captured by the sensors are increasing. Considering fast detection and high accuracy, the industry favors geometric key point-based solutions. However, there are a large number of small and fuzzy objects in the real world. Geometric key point detectors do not effectively utilize the contextual features of the region of interest, leading to excessive false positive and false negative results. In this work, a simple, effective, and interpretable tiny object detection method called Regional Cross Self-Attention Object Detection Network (RCSANet) is proposed. It adopts Region Proposal Networks and transformers to capture regional background relations and uses regional background relations to generate key point sequences. The regional cross self-attention mechanism is introduced to curtail computation redundancy and minimize the interference of redundant information to the target region. Additionally, a position coding called dynamic implicit position coding is proposed to cooperate with regional cross self-attentiveness. Dynamic implicit location coding can encode arbitrarily long input sequences. The computational cost of RCSANet is significantly lower than that of state-of-the-art object detection solutions. Moreover, RCSANet improves the performance on the four benchmark datasets, of MSCOCO, Tinyperson, DOTA, and AI-TOD, by about 3.0%AP.