FCMA-Det: Low-Light Image Object Detection Based on Feature Complementarity and Multicontent Aggregation

Aiming at the problems of limited feature expression and underutilization of contextual information in the object detection task in low-light environments, a low-light object detection algorithm based on feature complementarity and multicontent aggregation (FCMA-Det) is proposed in this article. The algorithm is based on the FCOS framework and improves the model performance through a stepwise optimization strategy. First, the feature complementary module (FCM) is designed. By dynamically fusing the dual-domain features of the original low-light image and the augmented image, the feature discriminative property of the augmented domain is strengthened while retaining the detail information of the original domain, so as to solve the problem of insufficient single-domain feature expression capability. Second, a multicontent aggregation module (MAM) is constructed. Combining multiscale context fusion and adaptive sensory field adjustment mechanism, it accurately captures the local details of the object and long-range context dependencies, and effectively copes with the challenges of complex background interference and insufficient feature extraction. In addition, feature consistency loss and dynamic weight fusion loss function are introduced to further optimize cross-domain feature alignment and multibranch collaborative training. Experiments on low-light dataset such as Exclusively Dark (ExDark) and small-object mixed-light dataset such as tiny object detection in aerial images (AI-TOD) show that the mean average precision (mAP) metric of FCMA-Det is improved by 1.7% on average compared with the existing SOTA method, which verifies the robustness and generalization ability of the algorithm under extreme light conditions.