Method for high density fry counting by image segmentation based on edge curvature

Fish quantity is one of the most meaningful tasks in aquaculture. Precise quantity can greatly contribute to the proper fish density in ponds, thus offering fair fish trade to sales and customers. However, fry (the juvenile fish) counting is still long-term hard work, due to the tiny size and huge batch in practice. This work aims to explore an efficient way for a precise number of fries. A self-built image acquisition device was designed and then applied by image segmentation using edge curvature. A whole closed space was constructed underneath with a white light background in the device. A camera inside was equipped downward to collect images of fries. The fries were in the size of 1-3 cm length and then placed in a 40 cm×30 cm×6 cm transparent box inside 0.9-1.1 cm depth water. Totally 18 000 fry images were acquired, which were of 6 planar density classes, including 0.25, 0.33, 0.42, 0.50, 0.58 and 0.67 fry/cm². The preprocessing was carried out to reduce the negative and redundant information in the collected images, such as the noises and the background. Therefore, the images were transferred to the binary images, and then the morphology operations like dilation and erosion were completed next. As the least self-contained unit in a binary image, the individual connected domains were extracted and then saved for the following deeper analysis, as well as their skeleton maps and edge maps. Among them, an individual connected domain was distinguished as a single fry or an adhesion of multiple fries. This identification was determined by two keys-one was the number of branch points of the skeleton map, and another was the distance from the points of the skeleton to the edge. Those domains that were recognized as single fry were directly counted as 1 into the amount. The number of fries needed to be reconfirmed in the domain with an adhesion of multiple fries. The detailed steps were listed as follow: 1) To calculate and filter, the curvature of the edge was purposed to search the points whose quantitative value always were negative, those points were named after ‘pit point’ and thought of as the places that overlaps appeared; 2) If the pit points of the cluster were adjacent and concentrated in a community, those pit points were marked as the pre-segmenting locations, while those locations were named as ‘pit point group’, and all of pit point group on an edge was found and numbered; 3) According to the different pre-segmenting locations, all forms of overlap were classified as three types, including crossed-fries, cascaded-fries, and single-pit cluster. Then potential overlaps were determined; 4) In each overlap, two highlighted segmentation points were picked out from the potential pit point groups, and then linked as a segmentation line to depart the original connected domain; 5) All segmentation lines were tested by presupposed rules to decide whether being saved or not. 6) The number of fries was set as the number of separate domains until all segmentation was realized in a connected domain. Finally, the fry number was calculated as the sum of the number from all domains in a complete image. The results show that the mean absolute percentage error (MAPE) was less than 1.37% after detection when the fry plane density was below 0.58 fry/cm². Meantime, both accuracy and efficiency were considered concurrently, if the plane density was kept within 0.33-0.58 fry/cm². The overlapping images of high-density fry can be expected to accurately and rapidly count over 1 cm length of fry. The finding can provide algorithmic support for the development of fry counters.