Automated Artemia Detection and Length Measurement Using Deep Convolutional Networks

Automated image analysis has attracted much attention in the bioscience engineering field, due to its superiority in processing efficiency and assessment objectivity over manual image analysis [10]. In aquaculture, the brine shrimp Artemia is an intensively used cost-effective diet for fish and crustacean larvae, and recently, the number of Artemia studies is increasing. Artemia objects are usually observed by a stereo-microscope, and conventionally, Artemia image analysis tasks are carried out manually, which is rather time-consuming and labor-intensive. It is highly desired to have tailor-made methods for analyzing Artemia images. In response to such demands, we focus on two typical Artemia image analysis tasks: Artemia detection and Artemia length measurement. In many Artemia studies, e.g., in a quality assessment of Artemia hatching, an automated method for detecting and counting the Artemia objects in images would be highly desired, but few works have addressed this challenging task. Recently, deep convolutional neural networks (CNN) have been adopted for object detection [3]. A representative method is the one combining a region proposal module with a deep convolutional neural network (R-CNN method) [2]. But this method entails a considerable redundancy [4], since it uses anchor boxes to generate thousands of region proposals. Inspired by the R-CNN method, we propose a so-called Marker-CNN method by combining a target marker proposal network with a CNN-based classifier [8], as illustrated in Fig. 1. In the first stage, instead of generating region proposals by blind anchor boxes, we design a target marker proposal network using a U-shaped fully convolutional network (UNet) architecture [5]. This module can indicate target candidates, separate adjacent objects and obtain the object structural information simultaneously. In the second stage, using a CNN architecture, we design a classifier to classify the target candidates into categories or label as a non-target, thereby obtaining the Artemia detection results. Furthermore, we have collected a dataset to train and test the proposed method. Experimental results on test images, which contain 1336 cysts and 3335 nauplii in total, manifest that the Marker-CNN method can accurately detect and count the Artemia objects in images, obtaining a detection accuracy of 99.6%. Samples of detection results are shown in Figs. 2(a) and 2(b). Artemia length is considered a key dependent variable in many Artemia studies [1]. For instance, in a controlled pond Artemia production, the Artemia length