Dataset - CQU_TMR

Road safety systems are essential for planning, managing, and improving road infrastructure and decreasing road accidents. Manual systems used for road safety assessments are inefficient, time consuming, and prone to error. Some automated systems using sensors, cameras, lidar, and radar to detect nearby obstacles such as vehicles, pedestrians, lane lines, some traffic signs and parking slots have been introduced to reduce road fatalities by minimizing human error. However, the existing road safety systems available in industry are unable to accurately detect all road safety attributes required by the Australian Road Assessment Program (AusRAP), a program launched to establish a safer road system through high-risk roads inspection, developing star ratings and safer roads investment plans to mitigate the possibility of meeting with accidents. Therefore, it is important to explore novel techniques and develop better automated systems which can accurately detect and classify all road safety attributes. This research focuses on the development of a novel deep learning technique for the analysis of road safety attributes. Various architectures, learning and optimisation techniques have been investigated to develop an appropriate deep learning-based technique that can detect road safety attributes with high accuracy. Firstly, a single-stage segmentation and classification technique to automatically identify AusRAP attributes has been investigated. Secondly, multi-stage segmentation and classification techniques using various classifiers have been investigated. Finally, Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO)-based techniques have been investigated to optimise the proposed deep learning techniques. The proposed techniques were evaluated on a real-world dataset using roadside videos provided by the Department of Transport and Main Roads (DTMR), Queensland, Australia, and Australian Road Research Board (ARRB). The classification accuracy was used as a metric to measure the performance, and to further validate the efficacy, different diversity measures such as specificity, sensitivity, and f1-score were used. An appropriate analysis and a comparison with existing techniques were conducted and presented. The results and analysis show that the proposed single-stage and multi-stage deep learning-based techniques achieve classification accuracy and misclassifications better than the existing state-of-the-art segmentation and classification techniques. It was found through experimentation that proposed single stage technique can avoid re-training the whole model using all training samples which requires a lot of time when a new attribute is introduced. Moreover, through extensive experimentation, it was found that it is not always necessarily required to have a large dataset for training. Effective solutions were found to eliminate the requirement to annotate large number of samples for each attribute to produce acceptable accuracy for industry. Both single-stage and multi-stage deep learning-based techniques were also validated using real world test data without cropping and pixel wise prediction was obtained for each object. The accurate location of the predicted object was known in predictions and hence, bounding box problem was avoided. Through the incorporation of optimisation techniques, optimum parameters suitable for road safety attributes were determined. The optimum parameters proved to be effective in terms of classification accuracy and time to achieve minimum error.道路安全系统对于道路基础设施的规划、管理与优化，以及降低道路交通事故发生率而言至关重要。传统的道路安全评估人工系统不仅效率低下、耗时冗长，且极易出现差错。目前已有部分采用传感器、摄像头、激光雷达（lidar）与雷达（radar）的自动化系统问世，可检测车辆、行人、车道线、部分交通标志及停车位等周边障碍物，通过减少人为失误来降低道路致死率。然而，当前工业界已有的道路安全系统，无法精准检测澳大利亚道路评估计划（Australian Road Assessment Program, AusRAP）所要求的全部道路安全属性。该计划旨在通过高危道路巡检、制定星级评级标准及安全道路投资方案，降低事故发生概率。因此，探索新型技术并开发可精准检测与分类所有道路安全属性的更优自动化系统，具有重要的研究价值。 本研究聚焦于开发一种用于道路安全属性分析的新型深度学习技术。为构建可高精度检测道路安全属性的适配型深度学习方法，研究团队对多种架构、学习与优化技术展开了探索。其一，探索了可自动识别AusRAP属性的单阶段分割与分类技术；其二，研究了采用多种分类器的多阶段分割与分类技术；最后，针对所提出的深度学习技术，分别探索了基于遗传算法（Genetic Algorithm, GA）与粒子群优化（Particle Swarm Optimisation, PSO）的优化方法。 本研究依托澳大利亚昆士兰州交通与主干道部（Department of Transport and Main Roads, DTMR）及澳大利亚道路研究委员会（Australian Road Research Board, ARRB）提供的路边视频真实数据集，对所提技术进行了性能评估。研究以分类准确率作为性能评估指标，并通过特异性、灵敏度及F1分数等多种泛化性指标进一步验证方法的有效性。研究开展了针对性的分析，并与现有技术进行了对比验证。结果与分析表明，所提出的单阶段与多阶段深度学习技术，在分类准确率与误分类率方面均优于现有前沿的分割与分类技术。实验结果显示，所提单阶段技术可避免在新增属性时，需使用全部训练样本重新训练整个模型的耗时操作。此外，通过大量实验验证，研究发现模型训练并非始终依赖大规模数据集。本研究找到了有效的解决方案，无需为每个属性标注大量样本，即可达到工业应用可接受的准确率。单阶段与多阶段深度学习技术均通过真实世界测试数据完成了验证，无需进行图像裁剪，且可对每个目标实现逐像素预测。预测结果中可直接获取目标的精准位置，因此规避了边界框相关的问题。通过引入优化技术，研究确定了适配道路安全属性的最优参数。经验证，该最优参数在提升分类准确率与降低训练耗时以实现最小误差方面均表现优异。