arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
arXiv:2607.05176v3 Announce Type: replace Abstract: Small object detection (SOD) remains a challenging task in real-world applications. Despite recent advances, existing detectors remain limited by r...
Despite the remarkable progress over the past decades, accurately identifying small objects remains challenging because of their insufficient visual cues. Previous works typically attempt to construct discriminative representation of the small objects.
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.
arXiv:2609.14437v1 Announce Type: cross Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
arXiv:2608.29167v1 Announce Type: new Abstract: Robustness of segmentation models is commonly assessed through input-domain perturbations, while dependence on frequency content within learned feature...
LiG-DETR introduces a Global-Local Reassembly framework for aerial object detection that captures high‑fidelity local features before compression and integrates them into a unified end‑to‑end DETR decoder. The method uses a shared encoder to extract both global and locally magnified features, reassembles the local features according to their spatial positions, and employs Context‑Preserved Selective Reassembly and Density‑Aware Adaptive Query Allocation to reduce redundant computation. Experiments demonstrate significant improvements on small and medium objects while maintaining strong performance on large objects, with favorable accuracy–efficiency trade‑offs and better cross‑domain generalization.
The paper introduces Cross-Scale Channel-wise Knowledge Distillation (CSCWD), a training-time framework that transfers high‑resolution spatial representations from a YOLO11m‑P2 teacher to a lightweight YOLO11n student without changing the student’s inference architecture. CSCWD aligns teacher P2 features with student P3 while also applying same‑scale distillation at deeper pyramid levels, yielding a 2.92‑point mAP@0.5 improvement over the baseline and a 2.09‑point gain over same‑scale distillation alone. In zero‑shot tests on DUT‑Anti‑UAV and on a Raspberry Pi 5, the 2.58‑million‑parameter student reaches 50.32% mAP@0.5 at 82.32 ms latency (12.15 fps) with negligible runtime or memory increase.
arXiv:2606. 31834v1 Announce Type: cross Abstract: Real-world detectors for autonomous driving, surveillance, and robotics must handle domain-shifts under strict latency and memory constraints, yet existing source-free object detection (SFOD) methods rely on heavyweight architectures that prioritize accuracy alone.
CF-YOLO introduces a real‑time detection framework for camouflaged micro‑defects on industrial components, combining a Context‑Perception Aggregation Module (CPAM) that fuses large‑kernel macro‑texture cues with small‑kernel boundary details, and a Feature Additive Refinement Module (FARM) that globally refines fine‑grained anomaly representations. The authors also release the Copper Tube Defect Dataset (CTDD), a benchmark of 1,847 images with 4,898 annotated defect boxes. Experiments show CF‑YOLO outperforms baseline detectors such as YOLOv11 by 2.2% in mAP@50 and 3.9% in Precision while preserving real‑time speed.
arXiv:2608.17623v2 Announce Type: replace Abstract: Retinal diseases are a leading cause of irreversible vision impairment, making early and accurate diagnosis essential for effective treatment. Opti...
The core challenge of heterogeneous change detection in remote sensing imagery lies in effectively decoupling genuine land-cover changes from significant modal disparities caused by distinct imaging mechanisms. These intrinsic inconsistencies are prone to introducing pseudo-changes, thereby constraining detection accuracy.