Underwater small object detection via automatic encoding pyramid with neighborhood information

With the rapid advancement of Internet of Things (IoT) technology, underwater sensor networks have shown significant potential in marine resource exploration, environmental monitoring, and related fields. However, detecting small objects in underwater images remains challenging due to complex conditions and the diminutive size of targets. The sparse features of small objects, combined with the pyramid structure of pooling and down-sampling, lead to the gradual loss of pixel-level features during propagation, thereby limiting detection performance. Moreover, the pyramid-style feature extraction network introduces information conflicts between features of different scales when integrating multi-density information, further degrading the detection accuracy of small objects in underwater images. To address these issues, we propose a novel feature pyramid network, termed AENI-USOD, which leverages neighborhood information and generates conflict suppression information to enhance the network’s focus on underwater objects. Specifically, we design a Neighborhood Information Fusion Module (NIFM) that employs multi-scale dilated convolutions to fuse features and generate neighborhood information for more precise object detection. To mitigate information conflicts during multi-scale feature fusion, we introduce a Conflict Suppression Module (CSM) that filters out conflicting interference, preventing small objects from being obscured by redundant information. Additionally, we implement the NMS-Circle post-processing method, which utilizes a circular feature attenuation function to reduce reliance on threshold parameters in soft non-maximum suppression. Our method is extensively evaluated on the UDD, RUOD, and URPC2019 datasets, demonstrating superior accuracy compared to state-of-the-art methods. This work provides robust technical support for intelligent object recognition within underwater IoT systems.