Camouflaged object detection (COD) is a valuable yet challenging task due to the resemblance between target objects and their surroundings. Existing methods have made impressive progress on COD by focusing on accurate segmentation of regions and boundaries. However, they still suffer from two major limitations: (1) lack of trade-offs between model accuracy and efficiency. (2) ignorance of the correlation between localization and edge cues. To tackle these issues, we propose a novel Edge-localization Cooperative Learning Network (ECLNet), which employs a compact encoder-decoder framework to effectively exploit the interaction between localization and edge cues. Through cooperative learning and compact structure design, ECLNet is enabled to extract comprehensive features with less computational costs, thus achieving a satisfactory trade-off between model accuracy and efficiency. Specifically, we propose an Edge-localization Interaction Module (EIM), which employs cooperative learning to simultaneously obtain edge details and enhanced localization perception. Moreover, we design a Hierarchical Feature Fusion Module (HFFM) to further fuse extracted features under the guidance of localization and edge cues. Extensive experiments on three challenging COD benchmarks demonstrate that ECLNet achieves more efficient performance compared to 14 state-of-the-art methods, improving 18.2% and 3% in terms of FPS and \(F^{\omega }_{\beta }\) on COD10K.