{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/sspnet-scale-selection-pyramid-network-for","title":"SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images","arxiv_id":"2107.01548","date":"2021-07-04","proceeding":null,"authors":["Mingbo Hong","Shuiwang Li","Yuchao Yang","Feiyu Zhu","Qijun Zhao","Li Lu"],"abstract":"With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects. Most existing methods employed Feature Pyramid Network (FPN) to enrich shallow layers' features by combing deep layers' contextual features. However, under the limitation of the inconsistency in gradient computation across different layers, the shallow layers in FPN are not fully exploited to detect tiny objects. In this paper, we propose a Scale Selection Pyramid network (SSPNet) for tiny person detection, which consists of three components: Context Attention Module (CAM), Scale Enhancement Module (SEM), and Scale Selection Module (SSM). CAM takes account of context information to produce hierarchical attention heatmaps. SEM highlights features of specific scales at different layers, leading the detector to focus on objects of specific scales instead of vast backgrounds. SSM exploits adjacent layers' relationships to fulfill suitable feature sharing between deep layers and shallow layers, thereby avoiding the inconsistency in gradient computation across different layers. Besides, we propose a Weighted Negative Sampling (WNS) strategy to guide the detector to select more representative samples. Experiments on the TinyPerson benchmark show that our method outperforms other state-of-the-art (SOTA) detectors.","url_abs":"https://arxiv.org/abs/2107.01548v1","url_pdf":"https://arxiv.org/pdf/2107.01548v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"sspnet-scale-selection-pyramid-network-for","repo_url":"https://github.com/MingboHong/SSPNet","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"sspnet-scale-selection-pyramid-network-for","repo_url":"https://github.com/MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"human-detection","task_name":"Human Detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"cam","method_name":"CAM"},{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2107.01548","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.01548"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MingboHong/SSPNet","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"unverified":4},"by_repo_kind":{"listed":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"b02d695104368fa4","entry":"IOD","repo":"MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images","repo_kind":"listed","path":"mmdet/core/evaluation/tiny_eval/merge.py","file_url":"https://github.com/MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images/blob/HEAD/mmdet/core/evaluation/tiny_eval/merge.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"b02d695104368fa4"}},{"code_sha256_prefix":"226df439d870e47c","entry":"torch_nms","repo":"MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images","repo_kind":"listed","path":"mmdet/core/evaluation/tiny_eval/bounding_box.py","file_url":"https://github.com/MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images/blob/HEAD/mmdet/core/evaluation/tiny_eval/bounding_box.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"226df439d870e47c"}},{"code_sha256_prefix":"c421f637bc6b7d70","entry":"xywh2xyxy","repo":"MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images","repo_kind":"listed","path":"mmdet/core/evaluation/tiny_eval/merge.py","file_url":"https://github.com/MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images/blob/HEAD/mmdet/core/evaluation/tiny_eval/merge.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c421f637bc6b7d70"}},{"code_sha256_prefix":"0c2f26cac95f7376","entry":"xyxy2xywh","repo":"MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images","repo_kind":"listed","path":"mmdet/core/evaluation/tiny_eval/split_and_merge_image.py","file_url":"https://github.com/MingboHong/SSPNet-Scale-Selection-Pyramid-Network-for-Tiny-Person-Detection-from-UAV-Images/blob/HEAD/mmdet/core/evaluation/tiny_eval/split_and_merge_image.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0c2f26cac95f7376"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}