Real-time characteristics of marine object detection under low light conditions: Marine object detection using YOLO with near infrared camera
Typ
Examensarbete för masterexamen
Program
Publicerad
2020
Författare
Emanuelsson, Emil
Wang, Lin
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This work discusses how a near infrared camera can be used to detect objects in a
marine environment. The goal is to identify marine objects in real-time under low
light conditions using object detection algorithm Yolo v3. Some different image processing
methods were analyzed, such as saliency, edge detection and convolutional
neural networks (CNN). Then the implementation of a scalable collision avoidance
was presented. The system uses Intel Realsense camera, OpenDLV software framework,
and the Linux operating system. For this work, an OpenDLV interface was
implemented for the camera, and OpenDLV perception microservice was used to
run its built-in Darknet-based Yolo v3 implementation.
Then the real-time characteristics of the system and the performance were evaluated.
It was proven that the system does not have real-time characteristics because of the
underlying OS and sensor communication protocol. The system achieved 0.71 mean
average precision (mAP) on boats with the test images. It was concluded that the
system still need more complete training and testing. Finally, a suggestion on how
to implement a similar system with real-time capabilities was given. This includes
changing camera, OS and some part of the software that was used.
Beskrivning
Ämne/nyckelord
object detection , CNN , neural network , NIR , camera , RTS , Yolo v3