The paper introduces TEEP‑RCNN, a two‑stage detector that augments Faster R‑CNN with a Feature Pyramid Network backbone and an enhanced Convolutional Block Attention Module (CBAM) featuring dropout in the channel attention MLP and batch‑norm in the spatial attention branch. Training employs a differential learning‑rate schedule with cosine‑annealing warm‑up, and inference uses Test‑Time Augmentation combined with Weighted Box Fusion to stabilize localization of elongated and boundary‑adjacent defects. On the NEU‑DET benchmark, TEEP‑RCNN attains 73.3 % mAP@50 and 37.9 % mAP@50‑95 in only ten epochs on a single GPU, matching or surpassing YOLOv11m while excelling on the rolled‑in‑scale defect category under the COCO metric.
By Kirtan Rajesh
arXiv:2608.22368v1 Announce Type: new
Abstract: While linear attention is a compelling mechanism for high-resolution object detection due to its reduced cost for global token mixing, converting the S...
By Huaiyuan Qin, Gabriel James Goenawan, Zihang Lin, Muli Yang, Hongyuan Zhu
arXiv:2603.11521v2 Announce Type: replace-cross
Abstract: Unsupervised Camouflaged Object Detection (UCOD) remains a challenging task due to the high intrinsic similarity between target objects and t...
By Shuo Jiang, Gaojia Zhang, Min Tan, Yufei Yin, Gang Pan
arXiv:2608. 19973v1 Announce Type: cross Abstract: Recently, open-vocabulary 3D object detection (3D-OVD) has gained increasing attention for its ability to detect unseen objects in 3D scenes.
By Shangbo Yuan, Jie Xu, Xiaofeng Zhu, Na Zhao
Background-Free Objectness Learning (B-FOR) is a dense, class‑agnostic detection framework that learns objectness without treating unlabeled regions as background. It predicts multi‑scale object‑center and scale fields, using spatially structured soft targets to supervise only reliable annotated areas and introduces displacement‑aware scale fields to model object extent. Experiments on PASCAL VOC, MS‑COCO, and Open Images show B‑FOR improves recall by over +10 AR points compared to prior class‑agnostic baselines, with ablation studies confirming the importance of localized supervision and displacement‑aware scaling.
By Dania Batool, Liliana Lo Presti, Marco La Cascia, Filippo Vella
arXiv:2608.20870v1 Announce Type: new
Abstract: Infrared small target detection is still challenging in remote sensing imagery, because the targets are extremely small, exhibit weak local contrast, a...
By Rui Liu, Jing Nie, Ying Fu
arXiv:2609.37331v1 Announce Type: new
Abstract: Balancing performance trade-offs on long-tailed data distributions remains a long-standing challenge in visual recognition. Existing methods mainly imp...
By Shenghan Chen, Yiming Liu, Zhipeng Deng, Haolin Wang, Jiale Zhou, Zhijian Wu, Xiankai Lu, Yafei Ou, Yefeng Zheng
OD3 introduces an optimization‑free dataset distillation framework tailored for object detection. The method first iteratively places object instances in synthesized images, then screens candidates with a pre‑trained observer model to discard low‑confidence objects. Applied to MS COCO and PASCAL VOC, OD3 achieves compression ratios from 0.25% to 5% and surpasses previous detection‑focused distillation methods by over 14% on COCO mAP50 at a 1.0% compression ratio.
By Salwa K. Al Khatib, Ahmed ElHagry, Shitong Shao, Zhiqiang Shen
arXiv:2608.23923v1 Announce Type: new
Abstract: Slicing-Aided Hyper Inference (SAHI) improves small object detection in high-resolution images but often spends substantial compute on background tiles...
By Rashid Riyadh, Abd Ullah Khan, Imad Gohar, Muzammil Behzad
arXiv:2609.26549v1 Announce Type: new
Abstract: Individual tree crown segmentation from aerial imagery underpins tree-level carbon accounting, biodiversity, and restoration monitoring at landscape sc...
By Thomas Pitts, Kunqi Li, Bin Liang
The paper introduces Structured Prior Knowledge (SPK), a framework that extracts and organizes latent priors from pretrained object detectors to improve out-of-distribution (OoD) detection. SPK uses in-distribution data and hallucination-inducing samples to elicit part-level semantic concepts, then combines these with geometric and contextual priors into a compact five-dimensional representation. Experiments across various detector architectures and OoD benchmarks show that SPK achieves state-of-the-art performance, demonstrating that pretrained detectors encode richer latent knowledge than previously exploited.
By Changshun Wu, Weicheng He, Xiaowei Huang, Saddek Bensalem
arXiv:2608.30727v1 Announce Type: cross
Abstract: Small-object detection under long-tailed data distributions is a fundamental yet challenging problem in multimedia. Railway Foreign Object Detection...
By Quan Hao, Ziyang Tao, Chenxi Zhang, Yudong Wang, Rui Shi, Liguo Zhang