Abstract

Accurate and efficient counting of shrimp larvae is crucial for monitoring reproduction patterns, assessing growth rates, and evaluating the performance of aquaculture. Traditional methods via density estimation are ineffective in the case of high density. In addition, the image contains bright spots utilizing the point light source or the line light source. Therefore, in this paper an automated shrimp counting platform based on optics and image processing is designed to complete the task of counting shrimp larvae. First, an area light source ensures a uniformly illuminated environment, which helps to obtain shrimp images with high resolution. Then, a counting algorithm based on improved ${ k}$-means and a side window filter (SWF) is designed to achieve an accurate number of shrimp in the lamp house. Specifically, the SWF technique is introduced to preserve the body contour of shrimp larvae, and eliminate noise, such as water impurities and eyes of shrimp larvae. Finally, shrimp larvae are divided into two groups, independent and interdependent, and counted separately. Experimental results show that the designed optical counting system is excellent in terms of visual effect and objective evaluation.

© 2023 Optica Publishing Group

ShrimpSeg: a local–global structure for the mantis shrimp point cloud segmentation network with contextual reasoning

Tengping Jiang, Shan Liu, Qinyu Zhang, Lin Zhao, Jian Sun, and Yongjun Wang
Appl. Opt. 62(10) D97-D103 (2023)

Dual-mode LiDAR SoC with an on-chip interframe filter and common optical platform design for keystone correction and auto-focus

Jier Wang, Hengwei Yu, and Patrick Yin Chiang
Appl. Opt. 62(29) 7658-7668 (2023)

ASA-BiSeNet: improved real-time approach for road lane semantic segmentation of low-light autonomous driving road scenes

Yang Liu, Fulong Yi, Yuhua Ma, and Yongfu Wang
Appl. Opt. 62(19) 5224-5235 (2023)

Detection of weak micro-scratches on aspherical lenses using a Gabor neural network and transfer learning

Hong-Thai Nguyen, Yu-Ming Tsao, and Hsiang-Chen Wang
Appl. Opt. 61(20) 6046-6056 (2022)

Improved STMask R-CNN-based defect detection model for automatic visual inspection of an optics lens

Haiyang Tang, Shan Liang, Dan Yao, and Yongjie Qiao
Appl. Opt. 62(33) 8869-8881 (2023)