Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations

• URL: http://arxiv.org/abs/2305.04516v1
• Date: Mon, 8 May 2023 07:22:15 GMT
• Title: Robust Traffic Light Detection Using Salience-Sensitive Loss: Computational Framework and Evaluations
• Authors: Ross Greer, Akshay Gopalkrishnan, Jacob Landgren, Lulua Rakla, Anish Gopalan, Mohan Trivedi
• Abstract summary: This paper proposes a traffic light detection model which focuses on defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights dataset, the first US traffic light dataset with an annotated salience property. We train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights.
• Score: 0.3061098887924466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most important tasks for ensuring safe autonomous driving systems is accurately detecting road traffic lights and accurately determining how they impact the driver's actions. In various real-world driving situations, a scene may have numerous traffic lights with varying levels of relevance to the driver, and thus, distinguishing and detecting the lights that are relevant to the driver and influence the driver's actions is a critical safety task. This paper proposes a traffic light detection model which focuses on this task by first defining salient lights as the lights that affect the driver's future decisions. We then use this salience property to construct the LAVA Salient Lights Dataset, the first US traffic light dataset with an annotated salience property. Subsequently, we train a Deformable DETR object detection transformer model using Salience-Sensitive Focal Loss to emphasize stronger performance on salient traffic lights, showing that a model trained with this loss function has stronger recall than one trained without.

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