Background: With the emerging application of autonomous vehicles in the automotive industry, several efforts have been made for the complete adoption of autonomous vehicles. One of the several problems in creating autonomous technology is the detection of water puddles, which can cause damages to internal components and the vehicle to lose control. This thesis focuses on the detection of water puddles on-road and off-road conditions with the use of Deep Learning models.

Objectives: The thesis focuses on finding suitable Deep Learning algorithms for detecting the water puddles, and then an experiment is performed with the chosen algorithms. The algorithms are then compared with each other based on the performance evaluation of the trained models.

Methods: The study uses a literature review to find the appropriate Deep Learning algorithms to answer the first research question, followed by conducting an experiment to compare and evaluate the selected algorithms. Metrics used to compare the algorithm include accuracy, precision, recall, f1 score, training time, and detection speed.

Results: The Literature Review indicated Faster R-CNN and SSD are suitable algorithms for object detection applications. The experimental results indicated that on the basis of accuracy, recall, and f1 score, the Faster R-CNN is a better performing algorithm. But on the basis of precision, training time, and detection speed, the SSD is a faster performing algorithm.

Conclusions: After carefully analyzing the results, Faster R-CNN is preferred for its better performance due to the fact that in a real-life scenario which the thesis aims at, the models to correctly predict the water puddles is key