Solutions to floating pollution with deep learning: A multi-camera joint-based method for floating object detection and tracking

Intelligent detection and tracking of surface objects is becoming increasingly important in water environment management. However, it remains a challenging problem in practical applications due to the complex environment, target scale and single-camera field of view issues. In this study, we propose a detection and tracking of floating targets method based on a multi-camera joint spatial strategy, which achieves intelligent monitoring of floating targets through interconnected surveillance cameras. Specifically, this study improves the network architecture of the Single Shot Multibox Detector (SSD) through the integration of a lightweight backbone network and feature pyramid network to improve the robustness of floating object detection. Then, a fast histogram of oriented gradient (FHOG) and a pyramid scale estimation strategy are introduced into the kernel correlation filter algorithm, and an improved image-matching algorithm is proposed to achieve accurate tracking and matching. Finally, a joint multi-camera relational optimization strategy is proposed to achieve continuous and accurate tracking of floating targets based on the single-camera parameter initialization. The proposed method is trained and compared with the state-of-the-art methods based on multiple scenarios. The comprehensive experimental results show that the proposed method can effectively cope with continuous detection and tracking in different scenarios, with IDF1, IDP and IDR reaching 87.23%, 89.37% and 84.37% respectively, and the speed reaching 29.83f/s. This work expands the intelligent detection and tracking of floating objects on the water surface to support integrated water environment management.