Underwater Sea Cucumber Target Detection Based on Edge-Enhanced Scaling YOLOv4

Abstract

Sea cucumber detection is widely recognized as the key to automatic culture. The underwater light environment is complex and easily obscured by mud, sand, reefs, and other underwater organisms. To date, research on sea cucumber detection has mostly concentrated on the distinction between prospective objects and the background. However, the key to proper distinction is the effective extraction of sea cucumber feature information. In this study, the edge-enhanced scaling You Only Look Once-v4 (YOLOv4) (ESYv4) was proposed for sea cucumber detection. By emphasizing the target features in a way that reduced the impact of different hues and brightness values underwater on the misjudgment of sea cucumbers, a bidirectional cascade network (BDCN) was used to extract the overall edge greyscale image in the image and add up the original RGB image as the detected input. Meanwhile, the YOLOv4 model for backbone detection is scaled, and the number of parameters is reduced to 48% of the original number of parameters. Validation results of 783 images indicated that the detection precision of positive sea cucumber samples reached 0.941. This improvement reflects that the algorithm is more effective to improve the edge feature information of the target. It thus contributes to the automatic multi-objective detection of underwater sea cucumbers.

Keywords

sea cucumber / edge extraction / feature enhancement / edge-enhanced scaling You Only Look Once-v4 (YOLOv4) (ESYv4) / model scaling