在智能汽车系统中高动态范围的音调映射
Ivana Shopovska1, Ana Stojkovic1, Jan Aelterman1
1IMEC-IPI-Ghent University, 9000 Ghent, Belgium.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
优化高动态范围 (HDR) 图像音色映射可以改善车辆中的弱势道路使用者 (VRU) 检测. 一种新的检测告知色调映射 (DI-TM) 方法提高了在具有挑战性的照明条件下对象检测性能.
科学领域:
- 计算机视觉 计算机视觉
- 汽车工程 汽车工程
- 图像处理 图像处理
背景情况:
- 智能驾驶辅助系统依赖于准确的弱势道路使用者 (VRU) 检测.
- 标准的成像传感器在高对比度照明下扎,限制了感知系统的性能.
- 高动态范围 (HDR) 成像提供了一个解决方案,但需要调色映射以与现有探测器兼容.
研究的目的:
- 评估色调映射对HDR图像对象检测性能的影响.
- 开发和优化HDR音色映射方法,在提高对象检测的同时保持自然图像外观.
- 为车辆感知系统引入一种新的检测信息音色映射 (DI-TM) 方法.
主要方法:
- 使用轻量级卷积神经网络 (CNN) 来将HDR视频的音调映射到标准的8位表示.
- 开发了一种新的训练方法,即检测告知音色映射 (DI-TM),集成对象检测反.
- 在各种场景条件下对DI-TM的有效性,稳定性和性能进行了评估,与最先进的音色映射方法相比.
主要成果:
- 拟议的DI-TM方法在具有挑战性的动态范围条件下显著改善了检测性能指标.
- 与其他方法相比,DI-TM在具有挑战性的条件下提高了13%的检测F2得分.
- 当将DI-TM与标准动态范围 (SDR) 图像进行比较时,观察到F2得分大幅增加49%.
结论:
- 基于检测的音色映射 (DI-TM) 是有效的优化车辆感知中的HDR成像.
- 在具有挑战性的照明场景中,DI-TM方法提供了卓越的物体检测性能.
- 这种方法提高了智能驾驶辅助系统的安全性和可靠性.
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