# Patch-based Selection and Refinement for Early Object Detection Source: [Patch-based Selection and Refinement for Early Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:26:06.010Z Authors: Tianyi Zhang, Kishore Kasichainula, Yaoxin Zhuo, Baoxin Li, Jae-Sun Seo, Yu Cao Published: 2023-11-03T23:41:13.000Z ## Abstract Early object detection (OD) is a crucial task for the safety of many dynamic systems. Current OD algorithms have limited success for small objects at a long distance. To improve the accuracy and efficiency of such a task, we propose a novel set of algorithms that divide the image into patches, select patches with objects at various scales, elaborate the details of a small object, and detect it as early as possible. Our approach is built upon a transformer-based network and integrates the diffusion model to improve the detection accuracy. As demonstrated on BDD100K, our algorithms enhance the mAP for small objects from 1.03 to 8.93, and reduce the data volume in computation by more than 77\\%. The source code is available at \\href{https://github.com/destiny301/dpr}{https://github.com/destiny301/dpr} ## 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. - [Patch Selection on VisDrone](): Patch Selection; VisDrone; Recall@Ratio (1%): 26.47. - [Patch Selection on DOTA](): Patch Selection; DOTA; Recall@Ratio (1%): 6.64. ## Original sources and implementation [Original paper]() [PDF]()