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Object Detection in Autonomous Vehicles: Status and Open Challenges

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arxiv 2201.07706 v1 pith:EOP2S4L3 submitted 2022-01-19 cs.CV cs.AIcs.LG

classification cs.CVcs.AIcs.LG
keywords objectautonomousdetectionvehicleschallengesdetectorsdrivingobjects
verification ladder T0 review T1 audit T2 compute T3 formal
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Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.

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Cited by 1 Pith paper

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  1. Task-Specific Zero-shot Quantization-Aware Training for Object Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A zero-shot quantization-aware training method for object detectors that synthesizes task-specific images with bounding-box labels via adaptive label sampling, then distills task-specific knowledge into the quantized network.

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