Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection

• URL: http://arxiv.org/abs/2604.06332v1
• Date: Tue, 07 Apr 2026 18:13:55 GMT
• Title: Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection
• Abstract summary: We introduce Telescope, a two-stage detection model designed for ultra-long range autonomous driving.<n>With 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.<n>Telescope achieves $76%$ relative improvement in mAP in ultra-long range detection compared to state-of-the-art methods.
• Score: 38.01832443563395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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