# Gender Identification of Chinese Mitten Crab Juveniles Based on Improved Faster R-CNN
## 1 Introduction  
The Chinese mitten crab [1], also known as the hairy crab or river crab, is native to East Asia. It predominantly inhabits rivers, estuaries, and coastal areas in China, Taiwan, South Korea, and Japan. Owing to its high market value and distinctive taste, it is regarded as an important commercial species and relished as a delicacy worldwide. China's river crab breeding output will reach 815,300 tons in 2022, with a total output value of more than 50 billion yuan (China Fishery Statistical Yearbook, 2022), playing an important role in its freshwater fisheries. Predominantly, pond culture, employing mixed-gender cultivation, is the principal method of Chinese river crab culture. Research shows notable differences in reproductive molting times and gonad development rates between males and females under these conditions [2]. Consequently, the market availability of adult male and female crabs varies. The optimal market period for female crabs in the Yangtze River Basin is from mid-to-late October to December. For male crabs, it is slightly later, extending from November to December [3]. Furthermore, due to differing reproductive molt times, maleand female crabs experience molting at different times. Soft-shell crabs, usually those that have recently molted, are vulnerable to attacks from hard-shell crabs, leading to decreased survival rates. This presents a significant challenge in the single-sex cultivation of river crabs, necessitating the development of efficient sex identification technology [4]. Currently, manual identification of juvenile crabs is labor-intensive and prone to errors [5], highlighting the need for automated and efficient sex identification technology to enhance the accuracy and productivity of Chinese mitten crab culture and fishery management [6]. In recent years, the development of deep learning and computer vision technology has brought revolutionary changes to target detection [7]. Machine learning technologies are increasingly replacing manual labor, becoming a key focus in future production. Therefore, target detection algorithms based on deep learning emerged as the times required. This type of algorithm can generally be divided into two categories: two-stage target detection algorithms and single-stage target detection algorithms [8; 9]. Two-stage algorithms, such as the R-CNN series (including R-CNN, Fast R-CNN, Faster R-CNN), Mask-RCNN, Cascade-RCNN, Libra-RCNN, and their variants [10; 11; 12], first generate candidate regions [13], then classify and regress bounding boxes in these regions. Known for their accuracy and robustness, they can precisely locate and classify targets through multiple iterations but are generally slower due to the additional candidate region generation step. On the other hand, single-stage algorithms like YOLO (You Only Look Once) and SSD (Single Shot MultiBox Detector) perform classification and bounding box regression in one step. YOLO directly outputs object category and location, achieving end-to-end detection [14], while SSD predicts object location and category on different scale feature maps for enhanced accuracy and speed [15]. However, these single-stage algorithms, while faster, often lack the accuracy and robustness of two-stage algorithms and are prone to missed or false detections. In this context, we have chosen to employ the R-CNN model. Our selection is driven by the model's proven efficacy in accurately detecting and classifying complex objects in diverse environments [16]. This choice aligns with our objective of differentiating between male and female Chinese mitten crab juveniles, a task that demands high precision due to the subtle physical differences between the sexes. The R-CNN model's robustness and accuracy, particularly in handling intricate image data, make it an ideal fit for our study. Furthermore, the model's ability to iteratively refine target location and classification enhances its suitability for our research aims.