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.