In Situ Sea Cucumber Detection across Multiple Underwater Scenes Based on Convolutional Neural Networks and Image Enhancements

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 4; p. 2037
• Main Authors: Wang, Yi, Fu, Boya, Fu, Longwen, Xia, Chunlei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 10.02.2023; MDPI
• Subjects: Accuracy; Algorithms; Aquaculture; Aquaculture industry; Artificial intelligence; Cameras; Computer vision; Datasets; Deep learning; Detectors; Efficiency; Fisheries; Investigations; Labor costs; Machine vision; marine organism recognition; marine survey; Methods; Neural networks; object detection; Organisms; Surveillance; Surveys; underwater image enhancement; YOLOv5; YOLOv7; marine survey; object detection; underwater image enhancement; YOLOv5; YOLOv7; marine organism recognition

Recently, rapidly developing artificial intelligence and computer vision techniques have provided technical solutions to promote production efficiency and reduce labor costs in aquaculture and marine resource surveys. Traditional manual surveys are being replaced by advanced intelligent technologies...