We present SweepLSD, a line segment detector that reads the image exactly once and emits each segment within a few rows of its last pixel passing the scan line. Every stage, including connected-compon...
Detectors for AI-generated video are evaluated offline. A clip is decoded to pixels and scored once, increasingly by a large vision-language model.
arXiv:2608. 07643v1 Announce Type: cross Abstract: Traffic data collection is dominated today by deep object detectors followed by tracking-by-detection, a pipeline that presupposes what is often missing in practice: a detector already trained on the class one wants to count.
arXiv:2607. 06600v1 Announce Type: cross Abstract: Line segment detection is a key building block in visual SLAM, 3D reconstruction, and industrial inspection.
arXiv:2609.08914v2 Announce Type: replace Abstract: Pixel-level annotation of fixed traffic-camera imagery is expensive, while crosswalk models trained from street-level imagery face a substantial vi...
arXiv:2608. 09519v1 Announce Type: cross Abstract: We present a reproducibility study of XFeat, a lightweight local feature extractor and matcher designed to identify corresponding points across images efficiently on resource-constrained hardware.
The paper introduces Adaptive Dual-Constrained Line Aggregation (ADLA), a framework that extracts line segments across multiple detection paradigms by aggregating pixels from an edge strength map under orientation coherence and bounded orthogonal distance constraints. ADLA dynamically updates line centroids and orientations, incorporates edge strength into various stages, and reduces parameter tuning. Experiments on generic, wireframe, and Manhattan line segment datasets show strong performance, achieving F^H scores of 0.8665, 0.8720, and 0.7297 respectively.
arXiv:2609.13551v1 Announce Type: new Abstract: Static-image benchmarks do not capture the computational and temporal requirements of practical orchard video analytics. This study presents an end-to-...
arXiv:2608. 11770v1 Announce Type: cross Abstract: Edge-deployed vision systems in target recognition, surveillance, autonomous vehicles, and drone domains require hierarchical inference pipelines where a detection model identifies objects of interest and downstream classifiers provide fine-grained attribute analysis.
arXiv:2607. 14568v1 Announce Type: cross Abstract: A companion study ran a 35B mixture-of-experts model on a 2011 NVIDIA Tesla C2075 (Fermi, sm_20, 6GB) as a GPU-prefill/CPU-decode hybrid, because the 4-bit model did not fit in device memory (arXiv:2606.
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
arXiv:2608.23923v1 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...