TY - GEN
T1 - YOLOv5 Model-based Ship Detection in High Resolution SAR Images
AU - Sapna, S.
AU - Sandhya, S.
AU - Shetty, Ramya D.
AU - Pais, Spurthy Maria
AU - Bhattacharjee, Shrutilipi
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Detection of ships in Synthetic Aperture Radar (SAR) images play a crucial role in maritime surveillance, most importantly under complex sea conditions. SAR permits observation in any weather conditions, at all hours of the day and night. At present, the ship detection from SAR images is a notable area of research since it is very difficult to detect the ships in the SAR images using traditional object or target detection algorithms. In this work, a You Only Look Once version 5 (YOLOv5) based ship detection model from SAR images with faster training speed and higher accuracy is implemented and tested. This model achieved a mean average precision (mAP) of 96.2% with a training time of 8.63 hours. This work also provides a comparative analysis with the existing methods for detection of ships in SAR images. The comparison shows that the YOLOv5 based model performs better in terms of both mean average precision and training time when compared to the existing models.
AB - Detection of ships in Synthetic Aperture Radar (SAR) images play a crucial role in maritime surveillance, most importantly under complex sea conditions. SAR permits observation in any weather conditions, at all hours of the day and night. At present, the ship detection from SAR images is a notable area of research since it is very difficult to detect the ships in the SAR images using traditional object or target detection algorithms. In this work, a You Only Look Once version 5 (YOLOv5) based ship detection model from SAR images with faster training speed and higher accuracy is implemented and tested. This model achieved a mean average precision (mAP) of 96.2% with a training time of 8.63 hours. This work also provides a comparative analysis with the existing methods for detection of ships in SAR images. The comparison shows that the YOLOv5 based model performs better in terms of both mean average precision and training time when compared to the existing models.
UR - https://www.scopus.com/pages/publications/85172656719
UR - https://www.scopus.com/pages/publications/85172656719#tab=citedBy
U2 - 10.1109/CONECCT57959.2023.10234764
DO - 10.1109/CONECCT57959.2023.10234764
M3 - Conference contribution
AN - SCOPUS:85172656719
T3 - Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies
BT - Proceedings of CONECCT 2023 - 9th International Conference on Electronics, Computing and Communication Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023
Y2 - 14 July 2023 through 16 July 2023
ER -