Method for segmentation of overlapping fish images in aquaculture

Chao Zhou,Chuanheng Sun,Xinting Yang,Daming Xu,Jintao Liu,Kai Lin,Song Zhang

doi:10.25165/j.ijabe.20191206.3217

Abstract

Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision system. In this paper, a method for segmentation of overlapping fish images in aquaculture was proposed. First, the shape factor was used to determine whether an overlap exists in the picture. Then, the corner points were extracted using the curvature scale space algorithm, and the skeleton obtained by the improved Zhang-Suen thinning algorithm. Finally, intersecting points were obtained, and the overlapped region was segmented. The results show that the average error rate and average segmentation efficiency of this method was 10% and 90%, respectively. Compared with the traditional watershed method, the separation point is accurate, and the segmentation accuracy is high. Thus, the proposed method achieves better performance in segmentation accuracy and effectiveness. This method can be applied to multi-target segmentation and fish behavior analysis systems, and it can effectively improve recognition precision. Keywords: aquaculture, image processing, overlapping segmentation, corner detection, improved Zhang-Suen algorithm DOI: 10.25165/j.ijabe.20191206.3217 Citation: Zhou C, Lin K, Xu D M, Liu J T, Zhang S, Sun C H, et al. Method for segmentation of overlapping fish images in aquaculture. Int J Agric & Biol Eng, 2019; 12(6): 135–142.

Highlights

Machine vision had been applied to all aspects of agriculture[1,2,3]
The overlapping of fish is one difficulty preventing the further application of machine vision to aquaculture, because, as shown previously, individual fish segmentation is a prerequisite for some significant machine vision applications, such as fish recognition, biometric measurements, biomass estimation, behavior tracking, etc.[6,7,8,9]
The structured light sensor has been used for automated multiple fish tracking in three-dimensions[11]

Summary

Introduction

Machine vision had been applied to all aspects of agriculture[1,2,3]. This technology is still not widely used in aquaculture and has not matured into a useful tool for many reasons[4,5]. Many studies had used machine vision for aquaculture. To avoids the overlapping of fish in image processing, the water depth of the experimental system used in the above studies was usually very low[12]. Compared to other industrial applications, many challenges must be overcome before using the machine vision for aquaculture. The interference caused by fish activity has limited the further application of this technology in aquaculture[13,14]

Methods

Results

Conclusion