Automatic shrimp counting method using local images and lightweight YOLOv4. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Lu; Zhou, Xinhui; Li, Beibei; Zhang, Hongxu; Duan, Qingling
• Is Part Of: Biosystems engineering. Volume 220(2022)
• Page Start: 39
• Page End: 54
• Publication Date: 2022-08
• Subjects: Shrimp counting -- Computer vision -- Deep learning -- Local image -- YOLOv4; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2022.05.011
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Shrimp counting is a fundamental operation for biomass estimation in shrimp culture. It is also vital for achieving reasonable feeding and improving breeding efficiency. The application of computer vision technology in the counting of aquatic products is nondestructive and highly efficient. However, owing to the small size, transparent body, and complex background of shrimp in the actual culture environment, existing methods cannot ensure the lightweight of the model applied while satisfying the counting accuracy, and it is difficult to achieve accurate real-time shrimp counting. Therefore, an automatic shrimp counting method using local images and lightweight YOLOv4 (Light-YOLOv4) was proposed in this study. Multiple local shrimp images were randomly cropped from the original top-view images previously collected using image processing technologies to construct a counting dataset. Subsequently, a local shrimp counting model based on Light-YOLOv4 is constructed and trained using transfer learning. Based on the trained model, the number of shrimp in each local shrimp image was predicted. The number of shrimp in the original shrimp image was obtained through a merging process, and the number of shrimp in the culture area was determined using the frame average method. The method was tested on a real shrimp dataset, and the Light-YOLOv4 local shrimp counting model achieved a counting precision of 92.12%, recall of 94.21%, F1 value of 93.15%, and mean average precision of 93.16%. Compared with other counting models, the proposed method exhibits a better comprehensive performance in terms of the counting accuracy, model size, and detection speed. Furthermore, when shrimp were counted within the entire culture area, the results were consistent with the true values. Highlights: An automatic shrimp counting method is proposed. A lightweight YOLOv4 model is constructed. The method reduces the time of image annotation in deep learning. The method accurately and stably realizes shrimp counting.