Ulit-BiDet: An Ultra-lightweight Object Detector for SAR Images Based on Binary Neural Networks

• Published in: IEEE transactions on geoscience and remote sensing p. 1
• Main Authors: Pu, Han, Zhu, Zhengwen, Hu, Qi, Wang, Dong
• Format: Journal Article
• Language: English
• Published: IEEE 04.03.2024
• Subjects: binarized quantization; Clutter; Detectors; Feature extraction; Marine vehicles; Neural networks; Object detection; Radar polarimetry; ship detection; synthetic aperture radar (SAR); Ultra-lightweight network
• ISSN: 0196-2892
• DOI: 10.1109/TGRS.2024.3373488

Synthetic aperture radar (SAR) target detection has extensively utilized convolutional neural networks (CNNs). Nonetheless, CNN-based methods often achieve favorable detection accuracy at the cost of high model complexity, hindering deployment of the algorithm in real-time application scenarios, such as maritime rescue and military decision making. To deal with this problem, we are the first to propose an ultra-lightweight object detector named Ulit-BiDet for SAR images, which incorporates binary neural network with very low storage and computation costs. First, we creatively design a performance and cost-scalable binary backbone that adapts to the diverse resources and computational capacities on practical devices. Then, the backbone structure is optimized with a new Non-Local Module to enhance semantic contextual information, thereby alleviating false detections caused by the interference from land clutter and sea clutter. Third, considering the SAR imaging mechanism, the interference near the ship boundary with similar scattering power probably affects the localization accuracy due to the interfered object-related contour information. To tackle the localization issue, we uniquely propose to utilize valuable and extra object-related contour semantics to guide representation learning of ship targets. The scheme compels the model to generate features that highlight object contour, thereby promoting accurate boundary localization in ship target detection. We validated the robustness of the proposed network in three mostly-cited publicly available datasets. Experimental results demonstrate that our model achieves 97.2%, 95.2%, and 77.3% detection accuracy with only 1.27M parameters and 0.22G OPs on SSDD, SAR-Ship, and AIR-SARShip2.0 ship detection datasets, respectively.