Biological Vision Inspired Context-Awareness Network for Various Non-Generic Object Detection

Object detection approaches are expanding by leaps and bounds with recent progress in deep learning. However, there is a considerable amount of environments hampering and challenging generic detectors in open-world scenarios, which received quite limited attention. In this paper, we focus on three specific challenging conditions: 1) targets presented with low lightness, 2) camouflaged objects merged in backgrounds, 3) complex acquisition scenarios, and present a novel end-to-end detector accordingly, termed Context-awareness Network (CANet). Specifically, we propose Global Context Encoder and Context Feature Fusion module to model the context-awareness (CA) mechanism that plays a crucial role in the human visual system (HVS) in an explicit way, which integrates both latent global and local context information to make each region of interest (RoI) more informative, and thus more discriminative. To our knowledge, such high-level mechanisms are under-explored for object detection in the literature. In addition, Global Semantic Awareness module is designed to regress positions and classify better in the process of extracting the feature. Experiments demonstrate that CANet achieves very competitive performance on the ExDark, DARK FACE, COD10K, and CURE-TSD, suggesting the effectiveness and efficiency of CANet in various challenging conditions as well as common scenarios.