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Volume 13 | Issue 6 | Year 2026 | Article Id. IJECE-V13I6P107 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I6P107

DATNet-RS: Domain-Adaptive Temporal Attention with Residual Shrinkage and Online PSO for Robust Object Detection in Adverse Weather

Joseph Rish Simenthy, Pallavi Singh

Received Revised Accepted Published
09 Mar 2026 08 Apr 2026 07 May 2026 27 Jun 2026

Citation :

Joseph Rish Simenthy, Pallavi Singh, "DATNet-RS: Domain-Adaptive Temporal Attention with Residual Shrinkage and Online PSO for Robust Object Detection in Adverse Weather," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 78-97, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P107

Abstract

Extreme weather conditions are a serious setback to the reliability of object detection in real-world driving conditions. This paper introduces DATNet-RS, a domain adaptive detection system that runs effectively in these conditions and does not require retraining or labelled weather data. A six-component multi-scale attention module is proposed, including local and global versions of channel, spatial, and temporal attention, which, in combination, reduce the noise-enhancing feature channels, highlight spatially consistent object regions, and capitalize on the frame-to-frame temporal continuity. Second, there is a common residual shrinkage denoising block on all levels of the feature pyramid to reduce the low-amplitude noise activations caused by weather, but not the structurally informative responses. Third, a gradient-free online inference-time adaptation scheme is added, where a small nine-dimensional parameter vector - controlling all attention magnitudes and shrinkage thresholds - is jointly optimized by the use of Particle Swarm Optimization (PSO). Experiments demonstrate that DATNet-RS continuously improves the performance compared to the baseline in all four adverse conditions and increases average mAP@0.5 to 75.1% (+4.3 percentage points) and average mAP@0.5:0.95 to 44.2% (+2.9 percentage points), in addition to maintaining real-time performance of GPU inference at about 50 FPS. On mAP at 0.5, the improvement of per-condition is between +3.9% (night) to +4.9% (rain). The assessment is done by isolating the contribution of each component in a seven-configuration ablation study and making comparisons between these and YOLOv9, RT-DETR, and Deformable DETR position DATNet-RS, a highly competitive real-time detector in the evaluated set.

Keywords

Object detection, Attention mechanisms, Temporal modeling, Residual Shrinkage, Particle Swarm Optimization, Feature Pyramid Network.

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