提出可学习的双曲聚焦机制，解决超远距离目标检测难题。
Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection
- 设计新型重采样层与图像变换，实现对极小目标的聚焦增强
- 在250米外距离上mAP提升至0.326，相对提升76%
- 计算开销小，适合实际车载系统部署
自动驾驶长途货运需在500米以上距离检测目标以满足高速制动需求。由于远距离物体在高分辨率图像中仅占少数像素，现有检测器性能显著下降。同时，商用激光雷达受距离导致分辨率二次衰减影响，有效探测范围不足。因此，基于图像的检测成为更具实用性的方案。本文提出Telescope，一种专为超远距离自动驾驶设计的两阶段检测模型。该模型结合强大检测主干网络与创新的重采样层及图像变换机制，有效应对远距离小目标检测挑战。相比当前最优方法，其在250米以上距离的mAP从0.185提升至0.326，相对提升76%，且计算开销极低，全距离段表现稳健。
原文摘要 · Abstract (English)
Autonomous highway driving, especially for long-haul heavy trucks, requires detecting objects at long ranges beyond 500 meters to satisfy braking distance requirements at high speeds. At long distances, vehicles and other critical objects occupy only a few pixels in high-resolution images, causing state-of-the-art object detectors to fail. This challenge is compounded by the limited effective range of commercially available LiDAR sensors, which fall short of ultra-long range thresholds because of quadratic loss of resolution with distance, making image-based detection the most practically scalable solution given commercially available sensor constraints. We introduce Telescope, a two-stage detection model designed for ultra-long range autonomous driving. Alongside a powerful detection backbone, this model contains a novel re-sampling layer and image transformation to address the fundamental challenges of detecting small, distant objects. Telescope achieves $76\%$ relative improvement in mAP in ultra-long range detection compared to state-of-the-art methods (improving from an absolute mAP of 0.185 to 0.326 at distances beyond 250 meters), requires minimal computational overhead, and maintains strong performance across all detection ranges.
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