An adaptive method of damage detection for fishing nets based on image processing technology

摘要

For offshore aquaculture cages, structural damage can cause serious economic property damage to the aquaculture industry, thus it is necessary to carry out regular inspections of the cage structure. At present, the main method of detecting netting damage is that the staff sneak into the water for manual investigation. This method not only does not guarantee personal safety, but is also labor-intensive and inefficient. This paper proposes a new feature curve method for non-contact underwater netting damage detection, which can effectively recognize damaged netting and analyze the degree of damage for the underwater net and return to its damage position. The new method presented in this paper was designed taking into consideration of effect of seaweed growth: Firstly, an image block within the ROI (region of interest) region was processed by bilateral filter. Secondly, the binary image was obtained via the OSTU (the maximum inter-class variance method) method and connected domain detection was performed. Thirdly, the feature gradient histogram was calculated according to the area of the mesh hole and the local peaks of the curve (named as feature curve) was searched to determine the position of damage in the netting. The proposed method combined the image processing technology with aquaculture engineering seamlessly, and reduced the complexity of the detection system greatly, and significantly improved the efficiency of the netting detection. Finally, the MATLAB program was developed to realize the netting detection process and the proposed method had been verified by the actual underwater netting experiment. The experimental results showed that the netting damage detection method proposed in this paper can successfully detect crack of the netting despite image degradation in water.