# Underwater Sea Cucumber Identification Based on Improved YOLOv5
## 1 Introduction  
With the improvement of living standards, people's demand for sea cucumbers and other sea treasures is increasing. Sea cucumbers have been considered health foods with extremely high nutritional value since ancient times and are widely used in the food and pharmaceutical industries. Sea cucumbers are mainly raised in captivity; bottom sowing culture is the main culture method, that is, sea cucumbers seedlings are sown on the seabed and harvested after they are grown. Due to the special growth environment of sea cucumbers, mainly relying on artificial diving for fishing, the harvesting process is dangerous, and the return is low, so aquaculture enterprises need automated equipment to replace manual operations [1]. The key point in the development of underwater automation equipment is the need to identify and fish underwater targets. The color of sea cucumbers and the color of the environment are closer, and the degree of differentiation is lower. In order to be able to capture sea cucumbers more precisely in the underwater environment, the research on the precise and rapid identification of sea cucumbers is of great value to the development of sea cucumber harvesting robots and the realization of automated underwater fishing. In the study of sea cucumber identification, many scholars have conducted research and exploration. [PERSON] et al. conducted research based on computer vision technology [2], conducted edge detection on underwater sea cucumber, extracted the centroid of sea cucumber thorns, and identified underwater sea cucumber by oval fit, with a precision of 93.33%. With the rapid development of related disciplines such as machine learning, it is also gradually being used in the field of sea cucumber identification. [PERSON] et al. proposed an underwater object detection algorithm based on improved Single ShotMultiBox Detector (SSD) [3], and the detection precision of this method reached 95.06%. [PERSON] et al. proposed underwater target recognition detection based on Cascade Region with CNN features (RCNN) [4], which effectively improved the recognition and detection effect of small targets by adjusting hyperparameters, with an average precision of 89.1%. [PERSON] proposed the Convolutional Neural Network (CNN)-based sea cucumber identification technology and its application in underwater robots [5], and combined the lightweight model MobileNet with the SSD model; the average precision reached 90.06%, the data storage method was optimized, and the model size was reduced by 75%. In the study of the underwater object detection algorithm based on Faster-RCNN proposed by [PERSON] et al., the deeper neural network ResNet-101 was replaced with the original VGG-16 network [6], and the average test precision of the dataset reached 63.03%. The above method uses the morphological characteristics, appearance characteristics, and characteristics of the sea cucumber that are different from the environmental color or combines multiple features to identify the sea cucumber. However, the vast majority of algorithms are aimed at the laboratory environment; because of the influence of aquatic plants, other marine animals and human garbage, there are too many uncertainties in the actual underwater environment in which the sea cucumber is located. Sea cucumbers are gregarious, and once there is overlap and occlusion, the recognition precision will be greatly reduced. In 2015, [PERSON] proposed the One-stage algorithm YOLOv1 [7]. RCNN as a classic object detection method, has an essential difference from YOLO in that the detection process of RCNN is divided into two steps: classification problem and regression problem. YOLO only operates on one step regression problem, so the YOLO algorithm processes images faster; YOLO uses full-graph information for training and prediction to reduce the error rate of prediction, and since the RCNN algorithm cannot take advantage of the entire image, plaques in the background may be mistakenly identified as targets during detection. [PERSON] et al.