The chosen platform is an automatic vehicle called Quick from SAIPA corporation in Iran. This product enjoys the benefits of being equipped with modern, comfort and safety-related features and is being produced with regards to international standards. Added features on this model include features such as push-button start system, 7-inch touch screen, cruise control, Bluetooth port, multi-mode driver power seat, automatic transmission system and numerous other options.
The platform is ready for test procedures. Required hardware is prepared and the CAN bus is used for data transmission. Furthermore, a camera is mounted behind the car front glass for further data gathering purposes.
Software for Image Based Object Detection100%
The software development process is done. A Deep learning based object detection software is developed for detection of objects and estimation of their distance to the car.
Data Set Gathering20%
Data Set Gathering is in progress. The prepared platform is being used for data set generation and data gathering in domestic urban environments in Tehran, Iran.
Object Detection with YOLO V2
Deep learning based object detection is performed by YOLO V2 without any training on the local road scene objects such as domestic cars. YOLO V2 detects objects on the Jetson TX2 in a 2 FPS with good accuracy. Tiny YOLO works on the Nvidia Jetson TX2 in a 17 FPS speed. The object detection results are acceptable but the accuracy is lower than YOLO. Using this method, cars are detected in a road scene on Sadr Highway, Tehran, Iran. The detected objects are marked by a green bounding box.
Video recording of the quick primary test on Sadr Highway, Tehran.