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
• DOI: 10.3390/s23042037
• ISSN: 1424-8220

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. However, underwater object detection and recognition are suffering from the image distortion and degradation issues. In this work, automatic monitoring of sea cucumber in natural conditions is implemented based on a state-of-the-art object detector, YOLOv7. To depress the image distortion and degradation issues, image enhancement methods are adopted to improve the accuracy and stability of sea cucumber detection across multiple underwater scenes. Five well-known image enhancement methods are employed to improve the detection performance of sea cucumber by YOLOv7 and YOLOv5. The effectiveness of these image enhancement methods is evaluated by experiments. Non-local image dehazing (NLD) was the most effective in sea cucumber detection from multiple underwater scenes for both YOLOv7 and YOLOv5. The best average precision (AP) of sea cucumber detection was 0.940, achieved by YOLOv7 with NLD. With NLD enhancement, the APs of YOLOv7 and YOLOv5 were increased by 1.1% and 1.6%, respectively. The best AP was 2.8% higher than YOLOv5 without image enhancement. Moreover, the real-time ability of YOLOv7 was examined and its average prediction time was 4.3 ms. Experimental results demonstrated that the proposed method can be applied to marine organism surveying by underwater mobile platforms or automatic analysis of underwater videos.