CampusDepth-A-Large-Scale-Day-Night-RGB-Dataset-for-Monocular-Depth-Estimation

Advanced Driver Assistance Systems (ADAS) require accurate and reliable perception of the surrounding environment to ensure vehicle safety and reduce the risk of collisions. Depth estimation plays a crucial role in understanding object distance and spatial relationships in traffic scenes. Traditional depth sensing approaches, such as stereo camera systems and LiDAR, provide accurate depth information but suffer from high cost, increased hardware complexity, calibration requirements, and limited suitability for low-cost embedded platforms. In recent years, monocular depth estimation using a single RGB camera has emerged as a promising alternative due to significant advancements in deep learning and computer vision techniques. This work focuses on the preliminary data analysis required for the development of a low-cost, camera-based anti-collision system using monocular depth estimation to address the limitations of conventional depth sensing methods. The study emphasizes the importance of conducting a comprehensive statistical and performance analysis on large-scale datasets collected across a finite spatial domain within the campus environment. The analysis aims to identify data trends, evaluate depth prediction consistency, and understand scenario-specific performance variations under different environmental and traffic conditions. Furthermore, the proposed methodology evaluates the collected campus dataset under Indian road conditions to examine the adaptability, robustness, and calibration requirements of the monocular depth estimation framework. The findings from this preliminary analysis are intended to support the efficient implementation, optimization, and future deployment of the proposed anti-collision system in real-world driving scenarios.高级驾驶辅助系统（Advanced Driver Assistance Systems，ADAS）需对周边环境实现精准可靠的感知，以此保障车辆行驶安全并降低碰撞事故风险。深度估计（depth estimation）在理解交通场景中物体距离与空间关系方面发挥着关键作用。传统深度感知方案，如立体相机系统与激光雷达（LiDAR），虽可提供精准的深度信息，但存在成本高昂、硬件复杂度提升、需满足校准需求，且难以适配低成本嵌入式平台等局限。近年来，依托深度学习与计算机视觉技术的显著进步，基于单台RGB相机（RGB camera）的单目深度估计（monocular depth estimation）已成为极具前景的替代方案。本研究聚焦于开发一套低成本、基于相机的防撞系统所需的前期数据分析工作，该系统采用单目深度估计技术，以解决传统深度感知方法的上述局限。 本研究强调，需对在校园环境内的有限空间域中采集的大规模数据集开展全面的统计与性能分析。该分析旨在识别数据趋势、评估深度预测的一致性，并探究不同环境与交通条件下特定场景的性能差异。此外，所提出的方法将在印度道路条件下对采集的校园数据集进行评估，以检验单目深度估计框架的适配性、鲁棒性与校准需求。本次初步分析的研究结果，将为所提出的防撞系统在真实驾驶场景中的高效实施、优化及后续部署提供支撑。