Row-Column Decoupled Loss: A Probability-Based Geometric Similarity Framework for Aerial Micro-Target Detection - Xiaohui Chen; Yunzhi Ling; Lingjun Chen; Li Liu; Xuechen Cui; Ziqiang Liu; Zhenyu Wang (IEEE Access)
Object detection, a fundamental research direction in the field of computer vision, serves as the basis for various complex visual tasks. However, detection performance for small targets remains significantly inferior compared to regular-sized targets. In conventional tiny object detection methods, the uniform distribution strategy adopted for bounding box regression during prediction processes tends to induce instability, particularly when handling small targets. To address this critical issue, this paper proposes Row-Column Decoupled Loss to enhance the stability of the prediction process and improve the accuracy of detection results. The proposed approach innovatively transforms the traditional regression paradigm by incorporating probabilistic modeling that better aligns with the spatial characteristics of small objects. Firstly, to address the challenge where the absence of overlap between predicted and ground-truth bounding boxes prevents distance computation in small object scenarios, this work introduces a probabilistic modeling framework based on Binomial distribution to characterize the spatial distribution of bounding boxes. This approach effectively captures the probabilistic attributes of pixel distributions within target regions. Secondly, we design a dual-channel evaluation mechanism, established a similarity evaluation strategy that better aligns with the target natural characteristics. Finally, Row-Column Decoupled Loss: A probability-Based Geometric Similarity Framework is proposed to optimize feature learning for minuscule targets. Experimental validation on the AI-TOD dataset demonstrates the efficacy of our method across multiple mainstream object detection frameworks and state-of-the-art small object detectors. Detectors equipped with Row-Column Decoupled Loss exhibit significant performance enhancements over baseline models, achieving a peak value of 60.5 in $AP_{0.5} $ and delivering a maximum improvement margin of 16.2 in $AP_{0.5} $ compared to baselines.