Abstract:
The paper presents a novel framework for automatic
classification and detection of waterborne vessels, tailored explicitly
to integrate with low-cost, low-power off-the-shelf sensors and
hardware. This framework demonstrates the practicality of incorporating
affordable hardware and sensors into unmanned surface
vehicles (USVs) to achieve dependable real-time surveillance and
reconnaissance capabilities. This initiative marks a significant
achievement as it is the first to successfully extract both auditory
and visual signatures of bottom trawling vessels, presenting
compelling evidence to identify vessels engaged in the detrimental
practice. The acoustic signal classification model utilizes the
Mel Frequency Cepstral Coefficients (MFCCs) and employs a
multi-class neural network model for accurate classification.
The proposed model achieves an impressive testing accuracy of
95.42%, highlighting the effectiveness of MFCCs in clustering
underwater acoustic signals. The visual component of the system
utilizes the YOLOv3-tiny model and is optimized to facilitate
faster inferencing. It is seamlessly integrated with the DeepSORT
tracking algorithm, enhancing the overall detection capabilities.
By combining the strengths of visual and acoustic subsystems, this
integrated approach overcomes the limitations of each component
individually. It provides a powerful solution for the detection
of vessels and activities while offering a practical approach to
maritime defence and ocean conservation
Citation:
P. Ranasinghe, A. Satharasinghe and R. Amarasinghe, "Integration of Low-Cost Sensing Systems for Autonomous Vessel Detection: Leveraging Acoustic and Vision Information," 2023 Moratuwa Engineering Research Conference (MERCon), Moratuwa, Sri Lanka, 2023, pp. 72-77, doi: 10.1109/MERCon60487.2023.10355509.