Background. The view of the Earth from above can offer a lot of data and with technological advancements in image sensors and high-resolution satellite images there is more quantity and quality of data which can be useful in research and applications like military, monitoring climate, etc. Deep neural networks have been successful in object detection and it is seen that their learning process can be improved with using right hyperparameters when configuring the networks. This can be done hyperparameter optimization by the use of genetic algorithms. Objectives. The thesis focuses on obtaining deep learning techniques with optimal hyperparameters using genetic algorithm to detect aircraft, vehicles and ships from satellite and aerial images and compare the optimal models with the original deep learning models. Methods. The study uses literature review to obtain the appropriate deep learning techniques for object detection in satellite and aerial images, followed by conducting experiments in order to implement a genetic algorithm to find the right hyperparameters and then followed by another experiment which compares the performance between optimal and original deep learning model on basis of performance metrics mentioned in the study. Results. The literature review results depict that deep learning techniques for object detection in satellite and aerial images are Faster R-CNN, SSD and YOLO. The results of experiments show that the genetic algorithm was successful in finding optimal hyperparameters. The accuracy achieved by optimized models was higher than the original models in the case of aircraft, vehicles and ship detection. The results also show that the training times for the models have been reduced with the use of optimal hyperparameters with slight decrease in precision when detecting ships. Conclusions. After analyzing all the results carefully, the best deep learning techniques to detect aircraft, vehicles and ships are found and stated. The implementation of the genetic algorithm has been successful as it provided a set of hyperparameters which resulted in the improvement of accuracy, precision and recall in all scenarios except for values of precision in ship detection as well as improvement in training times.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-22310 |
| Date | January 2021 |
| Creators | Thoudoju, Akshay Kumar |
| Publisher | Blekinge Tekniska Högskola, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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