YOLO-S: A Lightweight and Accurate YOLO-like Network for Small Target Selection in Aerial Imagery

• Published in: Sensors (Basel, Switzerland) Switzerland), 2023-02, Vol.23 (4), p.1865
• Main Authors: Betti, Alessandro, Tucci, Mauro
• Format: Article
• Language: English
• Subjects: Accuracy; Cognitive tasks; Datasets; Industrial applications; Rescue operations; Search and rescue missions; Search and rescue operations; Sensors; Unmanned aerial vehicles; Vehicles
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23041865

Small target detection is still a challenging task, especially when looking at fast and accurate solutions for mobile or edge applications. In this work, we present YOLO-S, a simple, fast, and efficient network. It exploits a small feature extractor, as well as skip connection, via both bypass and concatenation, and a reshape-passthrough layer to promote feature reuse across network and combine low-level positional information with more meaningful high-level information. Performances are evaluated on AIRES, a novel dataset acquired in Europe, and VEDAI, benchmarking the proposed YOLO-S architecture with four baselines. We also demonstrate that a transitional learning task over a combined dataset based on DOTAv2 and VEDAI can enhance the overall accuracy with respect to more general features transferred from COCO data. YOLO-S is from 25% to 50% faster than YOLOv3 and only 15-25% slower than Tiny-YOLOv3, outperforming also YOLOv3 by a 15% in terms of accuracy (mAP) on the VEDAI dataset. Simulations on SARD dataset also prove its suitability for search and rescue operations. In addition, YOLO-S has roughly 90% of Tiny-YOLOv3's parameters and one half FLOPs of YOLOv3, making possible the deployment for low-power industrial applications.