TY - GEN
T1 - Deep WaveNet-based YOLO V5 for Underwater Object Detection
AU - Balaji, Adithya
AU - Yogesh, S.
AU - Kalyaan, C. K.
AU - Narayanamoorthi, R.
AU - Dooly, Gerard
AU - Dhanalakshmi, Samiappan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Detecting objects under the water is always a difficult task. Due to the unique characteristics of underwater settings, identifying underwater objects can be difficult. When light penetrates deep within the water, the aqueous medium exhibits diffraction and scattering. This leads to murky, obscured films and photos, which make interpretation more difficult. Underwater object detection has become one of the most important objectives in deep-sea exploration. To overcome the above issues, we propose a methodology based on the YOLO model in which the images will be passed through for detection. The images are trained using the CNN-based deep wave net algorithm which enhances the faded or distorted image further making the YOLO algorithm infer better results. The YOLO models are compared with other detection models and different activation layers are also compared.
AB - Detecting objects under the water is always a difficult task. Due to the unique characteristics of underwater settings, identifying underwater objects can be difficult. When light penetrates deep within the water, the aqueous medium exhibits diffraction and scattering. This leads to murky, obscured films and photos, which make interpretation more difficult. Underwater object detection has become one of the most important objectives in deep-sea exploration. To overcome the above issues, we propose a methodology based on the YOLO model in which the images will be passed through for detection. The images are trained using the CNN-based deep wave net algorithm which enhances the faded or distorted image further making the YOLO algorithm infer better results. The YOLO models are compared with other detection models and different activation layers are also compared.
KW - Deep Learning
KW - Neural Networks
KW - Object Detection
UR - http://www.scopus.com/inward/record.url?scp=85173684420&partnerID=8YFLogxK
U2 - 10.1109/OCEANSLimerick52467.2023.10244645
DO - 10.1109/OCEANSLimerick52467.2023.10244645
M3 - Conference contribution
AN - SCOPUS:85173684420
T3 - OCEANS 2023 - Limerick, OCEANS Limerick 2023
BT - OCEANS 2023 - Limerick, OCEANS Limerick 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 OCEANS Limerick, OCEANS Limerick 2023
Y2 - 5 June 2023 through 8 June 2023
ER -