CIDNet: Cross-Scale Interference Mining Detection Network for underwater object detection

Abstract

Underwater object detection plays a crucial role in advancing marine economics, protecting the environment, and promoting the planet’s sustainable development. Compared to land-based scenes, underwater object detection is often hindered by color deviation and low visibility. To effectively address these interference issues, we propose a Cross-Scale Interference Mining Detection Network (CIDNet). We first extract multidimensional feature representations from the input images using a standard residual network backbone, which uses a deep structure and residual connectivity mechanism. We then refine these features through interference mining and cross-scale feature fusion strategies, and further enhance feature hierarchy levels using adaptive feature mapping optimization. In addition, we introduce three-dimensional convolution combination with a channel dimension unification strategy to enhance the fine-grained representation of hierarchical feature layers. Finally, the refined features are fed into a Task-aligned detection head module, which improves the detection accuracy by optimizing a collaboration between classification and localization tasks through a task-aligned learning strategy. Extensive experiments conducted on the DUO and COCO datasets demonstrate that our method effectively detects hidden objects in realistic underwater scenes and significantly outperforms current state-of-the-art methods in terms of accuracy. The codes and model weights will be available at https://www.researchgate.net/publication/390270613_CIDNet.