TY - JOUR
T1 - A review of intelligent ship marine object detection based on RGB camera
AU - Yang, Defu
AU - Solihin, Mahmud Iwan
AU - Zhao, Yawen
AU - Yao, Benchun
AU - Chen, Chaoran
AU - Cai, Bingyu
AU - Machmudah, Affiani
N1 - Publisher Copyright:
© 2023 The Authors. IET Image Processing published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.
PY - 2024/2/7
Y1 - 2024/2/7
N2 - The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost-effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi-feature extraction, multi-fusion technology, and lightweight detection architecture.
AB - The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost-effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi-feature extraction, multi-fusion technology, and lightweight detection architecture.
KW - intelligent transportation systems
KW - learning (artificial intelligence)
KW - marine navigation
KW - object detection
UR - http://www.scopus.com/inward/record.url?scp=85174385289&partnerID=8YFLogxK
U2 - 10.1049/ipr2.12959
DO - 10.1049/ipr2.12959
M3 - Review article
AN - SCOPUS:85174385289
SN - 1751-9659
VL - 18
SP - 281
EP - 297
JO - IET Image Processing
JF - IET Image Processing
IS - 2
ER -