TY - GEN
T1 - Underwater Object Detection Based on Enhanced YOLO
AU - Wang, Xiaohan
AU - Jiang, Xiaoyue
AU - Xia, Zhaoqiang
AU - Feng, Xiaoyi
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - As an important research topic in the field of computer vision, object detection has been successfully applied to several fields. YOLO is one of the popular frameworks for detection, but the traditional YOLO detection method lacks the processing of anchor points with detection and recognition features. In addition, most detection methods seldom consider of complex environments, especially for underwater images with high turbidity. Therefore, a YOLO based underwater object detection method for underwater images is proposed. An improved YOLO detection method without anchor points is introduced, where the detection features are separated from the recognition features to reduce the mutual interference between features and improve the detection accuracy. Further, a Retinex-based image enhancement algorithm is also proposed for underwater images enhancement. Relevant experiments based on underwater datasets are conducted to verify the effectiveness of the proposed enhanced YOLO detection method.
AB - As an important research topic in the field of computer vision, object detection has been successfully applied to several fields. YOLO is one of the popular frameworks for detection, but the traditional YOLO detection method lacks the processing of anchor points with detection and recognition features. In addition, most detection methods seldom consider of complex environments, especially for underwater images with high turbidity. Therefore, a YOLO based underwater object detection method for underwater images is proposed. An improved YOLO detection method without anchor points is introduced, where the detection features are separated from the recognition features to reduce the mutual interference between features and improve the detection accuracy. Further, a Retinex-based image enhancement algorithm is also proposed for underwater images enhancement. Relevant experiments based on underwater datasets are conducted to verify the effectiveness of the proposed enhanced YOLO detection method.
KW - YOLO
KW - object detection
KW - underwater
UR - https://www.scopus.com/pages/publications/85139227625
U2 - 10.1109/ICIPMC55686.2022.00012
DO - 10.1109/ICIPMC55686.2022.00012
M3 - 会议稿件
AN - SCOPUS:85139227625
T3 - Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
SP - 17
EP - 21
BT - Proceedings - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Image Processing and Media Computing, ICIPMC 2022
Y2 - 27 May 2022 through 29 May 2022
ER -