EDCAANet: A lightweight COD network based on edge detection and coordinate attention assistance

Abstract

In order to obtain the higher efficiency and the more accuracy in camouflaged object detection (COD), a lightweight COD network based on edge detection and coordinate attention assistance (EDCAANet) is presented in this paper. Firstly, an Integrated Edge and Global Context Information Module (IEGC) is proposed, which uses edge detection as an auxiliary means to collaborate with the atrous spatial convolution pooling pyramid (ASPP) for obtaining global context information to achieve the preliminary positioning of the camouflaged object. Then, the Receptive Field Module based on Coordinate Attention (RFMC) is put forward, in which the Coordinate Attention (CA) mechanism is employed as another aid means to expand receptive ffeld features and then achieve global comprehensive of the image. In the final stage of feature fusion, the proposed lightweight Adjacent and Global Context Focusing module (AGCF) is employed to aggregate the multi-scale semantic features output by RFMC at adjacent levels and the global context features output by IEGC. These aggregated features are mainly refined by the proposed Multi Scale Convolutional Aggregation (MSDA) blocks in the module, allowing features to interact and combine at various scales to ultimately produce prediction results. The experiments include performance comparison experiment, testing in complex background, generalization experiment, as well as ablation experiment and complexity analysis. Four public datasets are adopted for experiments, four recognized COD metrics are employed for performance evaluation, 3 backbone networks and 18 methods are used for comparison. The experimental results show that the proposed method can obtain both the more excellent detection performance and the higher efficiency.