Robust Maritime Object Detection under Adverse Conditions via Joint Semantic Learning without Extra Computational Overhead

Abstract

This study addresses the challenge of robust object detection in maritime environments, where dynamic conditions such as fog, brightness variations, and motion blur can degrade accuracy. We propose a novel framework, Joint Semantic Learning (JSL), which combines ocean scene segmentation and object detection to improve both performance and robustness. JSL incorporates the ocean scene segmentation module into the detection network during training and removes it during inference, ensuring no additional computational overhead. Through ocean scene segmentation, the feature extractor learns to understand the overall context of the image and extract detailed information about objects. Extensive experiments show that JSL, applied to various convolutional neural network-based detectors, achieves significant performance improvements on maritime datasets SMD and SeaShips. Notably, the proposed method shows substantial performance gains on the SMD-C and SeaShips-C datasets, which include adverse conditions, demonstrating the robustness of the proposed method. Furthermore, experiments comparing our method with existing state-of-the-art multi-task methods on the Cityscapes dataset validate its effectiveness in generalizing to urban environments. The efficient integration of spatial and semantic information of JSL ensures accurate and reliable object detection across diverse applications. Our code is available at: https://github.com/gistailab/JSL. © 2025 IEEE.