Title: Slim Object Direct Offset (SODO): Why YOLO with TOOD struggles to detect slim objects? A new matching approach for slim objects
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: YOLO, Object detection, TOOD, matching mechanism

Abstract: The YOLO model has become the mainstream in object detection. The latest YOLOv8 to YOLOv13 models all use TOOD as the target matching mechanism and have achieved excellent performance in object detection tasks. However, certain limitations remain. Specifically, when detecting slim objects (bounding box (bbox) aspect ratio $> 20$), it is difficult for TOOD to match the grids. This is due to TOOD’s matching mechanism. According to TOOD matching mechanism, for slim objects, they are likely to not match any grid in the large or medium detector, as slim bboxes probably do not pass any grid center. To solve this problem, we propose the Slim Object Direct Offset (SODO). By designing strategies like Slim Object Assignment (SOA), Direct Offset, Center Offset, and Layer Wise Assignment (LWA), slim objects can match grids in the large or medium detector. Our SODO method outperforms the baseline model (YOLOv11-L) on both COCO (+0.3\% mAP@50) and COCOSlim (+1\% mAP@50) datasets. Additionally, for slim objects, significant performance improvements are achieved on our synthetic slim object dataset and multiple public datasets from Roboflow, including pole\_detection and tans.