# Segmentation is All You Need Source: [Segmentation is All You Need]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:09:33.534Z Authors: Zehua Cheng, Yuxiang Wu, Zhenghua Xu, Thomas Lukasiewicz, Weiyang Wang Published: 2019-04-30T15:13:01.000Z ## Abstract Region proposal mechanisms are essential for existing deep learning approaches to object detection in images. Although they can generally achieve a good detection performance under normal circumstances, their recall in a scene with extreme cases is unacceptably low. This is mainly because bounding box annotations contain much environment noise information, and non-maximum suppression (NMS) is required to select target boxes. Therefore, in this paper, we propose the first anchor-free and NMS-free object detection model called weakly supervised multimodal annotation segmentation (WSMA-Seg), which utilizes segmentation models to achieve an accurate and robust object detection without NMS. In WSMA-Seg, multimodal annotations are proposed to achieve an instance-aware segmentation using weakly supervised bounding boxes; we also develop a run-data-based following algorithm to trace contours of objects. In addition, we propose a multi-scale pooling segmentation (MSP-Seg) as the underlying segmentation model of WSMA-Seg to achieve a more accurate segmentation and to enhance the detection accuracy of WSMA-Seg. Experimental results on multiple datasets show that the proposed WSMA-Seg approach outperforms the state-of-the-art detectors. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Detection on Rebar Head](): Object Detection; Rebar Head; F1 Score: 98.83. - [Object Detection on MS COCO (test-dev)](): Object Detection; MS COCO (test-dev); mAP@.5: 58.2. ## Original sources and implementation [Original paper]() [PDF]()