- author = "Dylon Zeng and Ying Bi and Ivy Liu and Bing Xue and Ross Vennell and Mengjie Zhang",
- booktitle = "2023 38th International Conference on Image and Vision Computing New Zealand (IVCNZ)",
- title = "A New Genetic Programming-Based Approach to Object Detection in Mussel Farm Images",
- year = "2023",
- address = "Palmerston North, New Zealand",
- month = "29-30 " # nov,
- keywords = "genetic algorithms, genetic programming, Image quality, Training, Industries, Image segmentation, Sensitivity, Object detection, Production, Object Detection, Waterline Detection",
- ISSN = "2151-2205",
- isbn13 = "979-8-3503-7052-2",
-
DOI = "
doi:10.1109/IVCNZ61134.2023.10343758",
- size = "6 pages",
- abstract = "Mussel farms are vital contributors to New Zealand's booming aquaculture industry, driving economic growth and generating employment opportunities across the country. To meet the demand for sustainable mussel production, innovative computer vision and AI solutions are essential in detecting mussel floats that maintain crop line buoyancy. However, object detection in mussel farm images faces considerable challenges such as poor image quality, partial occlusion, and high variability in image conditions. In this paper, we propose a new genetic programming-based approach tailored for object detection in mussel farm images. The approach features a new waterline detection algorithm for image segmentation and a new genetic programming method, called 3-Tree GP, for object detection (mussel buoy/float). Experimental results show that the proposed approach achieves a detection F1 score of 94.4percent, surpassing the state-of-the-art YOLOv8 by over 4percent. In particular, the approach excels in identifying objects that are partially occluded, or located far away from the camera, making it suitable for real-world applications.",
-
notes = "Also known as \cite{10343758},
See also \cite{Dylon_Zeng:JRSNZ}",
