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

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 783images 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.

A Clustering Method Based on Brain Storm Optimization Algorithm

In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.