Real-time dense small object detection model for floating litter detection and removal on water surfaces.

Detecting dense, small litter on water surfaces presents a significant challenge in the field of unmanned litter-cleaning vehicles. Dense small objects on water surfaces are easily influenced by various factors, including ripples, reflections, and changing lighting conditions. Existing detection methods often fail to effectively mine multidimensional global features and tend to overlook the impact of feature conflicts on small object detection. To overcome these challenges, we propose the Dense Small Floating Litter Detection Network (DSFLDNet), which incorporates multidimensional attention mechanisms in both the spatial and frequency domains. We have designed spatial and channel attention modules that utilize multiple sets of orthogonal frequency filters to enhance the network's sensitivity to small objects against complex water surface backgrounds. In our backbone architecture, we enhance feature extraction capabilities through parallel information extraction and channel blending techniques. A feature fusion approach that combines a feature pyramid with multidimensional attention mechanisms is implemented to mitigate conflicts between features at different levels, thereby improving overall detection accuracy. The proposed model demonstrates optimal experimental performance on both custom private datasets and publicly available data. Specifically, it achieves an accuracy of 93.1 %, a recall rate of 93.9 %, and a mean average precision of 97.3 % on the Dense Small Object Datasets, with a processing frame rate of 107 frames per second. Moreover, this model has been successfully deployed on an unmanned vessel for real-time detection, proving to be an effective tool for cleaning and recycling debris from water surfaces.