YOLO-SPD: Tiny objects localization on remote sensing based on You Only Look Once and Space-to-Depth Convolution
Pei-Hsiang Hsu, Pei‐Jun Lee, Trong-An Bui, Yi-Shau Chou · 2024
Detecting tiny objects in remote sensing images presents a significant challenge, especially moving vehicles or ships smaller than $32 \times 32$ pixels. This paper uses Space-to-depth Convolution (SPD-Conv), an architecture design to increase feature extraction of tiny objects. Additionally, the proposed network adopts four detection heads to enhance the tiny object bounding boxes. Compared to the baseline model the proposed network increases by 14.3% of Average Precision at 50% intersection over union (AP50).