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...
Albireo is an adaptive, energy‑efficient inference framework for video object detection on edge devices that wraps existing detectors without modification. It uses a 10‑dimensional Kalman filter per active object to decide when to skip detector calls, predicting bounding boxes on skipped frames at near‑zero GPU cost. Evaluated on BDD100K with YOLO and RF‑DETR detectors on NVIDIA Jetson AGX Thor and Orin, Albireo maintains AP@50 within ±1.2 pp of full‑frame inference while reducing energy consumption by 12.1–17.6 % and improving accuracy for some models.
arXiv:2611.06436v1 Announce Type: cross Abstract: We show that a hardwired distributed processing unit (HDPU) can take a neural decision at the sensor and so remove the inference from the host. Built...
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:2610.25068v2 Announce Type: replace-cross Abstract: Resource-constrained robots and autonomous vehicles need scene memories that localize a camera and render nearby views without a desktop GPU....
FALCON is a camera‑based 3D occupancy front‑end built on the AMD Kria KR260 FPGA that provides explicit latency guarantees for autonomous driving perception. The system uses cycle‑bounded programmable‑logic compute, a measured inference envelope over 100k frames, and a watchdog to flag stale outputs, achieving all 300k proxy frames within a 100 ms deadline plus at most 10.2 ms. On the full FlashOcc pipeline the board attains 29.88 mIoU on the Occ3D‑nuScenes set, matching 92.8 % of an FP32 baseline, while maintaining tighter tail latency than competing Jetson Orin and datacenter GPUs.
The paper introduces NPLSD, a pair of line‑segment detectors optimized for the Neural‑ART NPU on STM32N6 microcontrollers. NPLSD‑H retains a convolutional backbone and replaces transformer components with a fully‑convolutional head, while NPLSD‑M adapts a lightweight trunk to the NPU’s supported operators. Trained on ImageNet and ShanghaiTech Wireframe, the models achieve competitive accuracy with 2.63 M and 0.62 M parameters, respectively, and ablation studies show initialization contributes significantly to performance.
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.