IoT Prism Lab
Research Lab at University of Bologna
Theses
Theses
2018 |
Valentini, Alice Evaluation of deep learning techniques for object detection on embedded systems Masters Thesis 2018. Abstract | Links | BibTeX | Tags: area surveying, deep learning, drones, DRONI, Machine Learning, object detection, UAV, yolo @mastersthesis{amslaurea15478, title = {Evaluation of deep learning techniques for object detection on embedded systems}, author = {Alice Valentini}, url = {https://amslaurea.unibo.it/id/eprint/15478}, year = {2018}, date = {2018-01-01}, abstract = {Area surveying is an important tool used to inspect and study in detail a given area, it is especially useful to monitor the movements and the settlement of populations located in a developing country. Unmanned Aerial Vehicles (UAV), given the recent developments, could represent a suitable technology in order to carry out this task in an easier and cheaper way. The use of UAV based surveys techniques poses many challenges in terms of accuracy, speed and efficiency. The target is to build an autonomous flight system which is able to define optimal flight paths using the gathered information from the environment. In this thesis we will focus on the development of the perception system which has to capture the desired information with accurate and fast detections. More in detail, we will explore and evaluate the use of object detection models based on Deep Learning techniques who will sense and collect data which will later use for on-board elaboration. The object detection model has to be accurate in order to detect all the objects encountered on the ground and fast in order to not introduce too much latency into the on-board decision system. Fast and accurate decisions could permit an efficient coverage of the area. Different embedded platforms will be considered and examined in order to meet the model's computational requirements and to provide an efficient use in terms of battery consumption. Different training configurations will be tested in order to maximize our detection accuracy metric, minimum average precision (mAP). The detection speed will be then evaluated on our board using Frame Per Second (FPS) metric. In addition to YOLO we also tested TinyYOLO, a smaller and faster network. Results will be then compared in order to find the best configuration in terms of accuracy/speed. We will show that our system is able to meet all the requirements even if we do not achieve our ideal detection speed.}, keywords = {area surveying, deep learning, drones, DRONI, Machine Learning, object detection, UAV, yolo}, pubstate = {published}, tppubtype = {mastersthesis} } Area surveying is an important tool used to inspect and study in detail a given area, it is especially useful to monitor the movements and the settlement of populations located in a developing country. Unmanned Aerial Vehicles (UAV), given the recent developments, could represent a suitable technology in order to carry out this task in an easier and cheaper way. The use of UAV based surveys techniques poses many challenges in terms of accuracy, speed and efficiency. The target is to build an autonomous flight system which is able to define optimal flight paths using the gathered information from the environment. In this thesis we will focus on the development of the perception system which has to capture the desired information with accurate and fast detections. More in detail, we will explore and evaluate the use of object detection models based on Deep Learning techniques who will sense and collect data which will later use for on-board elaboration. The object detection model has to be accurate in order to detect all the objects encountered on the ground and fast in order to not introduce too much latency into the on-board decision system. Fast and accurate decisions could permit an efficient coverage of the area. Different embedded platforms will be considered and examined in order to meet the model's computational requirements and to provide an efficient use in terms of battery consumption. Different training configurations will be tested in order to maximize our detection accuracy metric, minimum average precision (mAP). The detection speed will be then evaluated on our board using Frame Per Second (FPS) metric. In addition to YOLO we also tested TinyYOLO, a smaller and faster network. Results will be then compared in order to find the best configuration in terms of accuracy/speed. We will show that our system is able to meet all the requirements even if we do not achieve our ideal detection speed. |