Background. The driving conditions of construction vehicles and their surrounding environment is different from the traditional transportation vehicles. As a result,they face unique challenges while operating in the construction/evacuation sites.Therefore, there needs to be research carried-out to address these challenges while implementing autonomous driving, although the learning approach for construction vehicles is the same as for traditional transportation vehicles such as cars.

Objectives. The following objectives have been identified to fulfil the aim of this thesis work. To identify suitable and highly efficient CNN models for real-time object recognition and tracking of construction vehicles. Evaluate the classification performance of these CNN models. Compare the results among one another and present the results.

Methods. To answer the research questions, Literature review and Experiment have been identified as the appropriate research methodologies. Literature review has been performed to identify suitable object detection models for real-time object recognition and tracking. Following this, experiments have been conducted to evaluate the performance of the selected object detection models.

Results. Faster R-CNN model, YOLOv3 and Tiny-YOLOv3 have been identified from the literature review as the most suitable and efficient algorithms for detecting and tracking scaled construction vehicles in real-time. The classification performance of these algorithms has been calculated and compared with each other. The results have been presented.

Conclusions. The F1 score and accuracy of YOLOv3 has been found to be better amongst the algorithms, followed by Faster R-CNN. Therefore, it has been concluded that YOLOv3 is the best algorithm in the real-time detection and tracking of scaled construction vehicles. The results are similar to the classification performance comparison of these three algorithms provided in the literature.