Computer vision technique for freshness estimation from segmented eye of fish image

Banwari, Anamika; Joshi, Rakesh Chandra; Dutta, Maitreyee

doi:10.1016/j.ecoinf.2022.101602

Cited by 29 publications

(2 citation statements)

References 32 publications

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“…16 Computer vision techniques have been widely adopted for aquaculture farming [17][18][19][20][21][22] and seafood processing industries. [23][24][25][26][27] To realize the realtime or near real-time application and achieve the needed accuracy, 28 artificial intelligence (AI)-driven machine learning and deep learning techniques have been integrated with the computer vision system, such as object detection [29][30][31] and semantic/instance segmentation. [32][33][34] Therefore, it is crucial to develop a robust and efficient computer vision system with the integration of deep learning technique to further automate the catfish cutting process.…”

Section: Introductionmentioning

confidence: 99%

Automating catfish cutting process using deep learning-based semantic segmentation

Thayananthan

Zhang

Liu

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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Mississippi and Alabama are the top two states producing and processing catfish in the United States, with the annual production of $382 million in 2022. The catfish industry supplies protein-rich catfish products to the U.S. market and contributes considerably to the development of the local economy. However, the traditional catfish processing heavily relies on human labors leading to a high demand of workforce in the processing facilities. De-heading, gutting, portioning, filleting, skinning, and trimming are the main steps of the catfish processing, which normally require blade-based cutting device (e.g., metal blades) to handle. The blade-based manual catfish processing might lead to product contamination, considerable fish meat waste, and low yield of catfish fillet depending on the workers’ skill levels. Furthermore, operating the cutting devices may expose the human labors to undesired work accidents. Therefore, automated catfish cutting process appears to be an alternative and promising solution with minimal involvement of human labors. To further enable, assist, and automate the catfish cutting technique in near real-time, this study presents a novel computer vision-based sensing system for segmenting the catfish into different target parts using deep learning and semantic segmentation. In this study, 396 raw and augmented catfish images were used to train, validate, and test five state-of-the-art deep learning semantic segmentation models, including BEiTV1, SegFormer-B0, SegFormer-B5, ViT-Adapter and PSPNet. Five classes were pre-defined for the segmentation, which could effectively guide the cutting system to locate the target, including the head, body, fins, tail of the catfish, and the image background. Overall, BEiTV1 demonstrated the poorest performance with 77.3% of mIoU (mean intersection-over-union) and 86.7% of MPA (mean pixel accuracy) among all tested models using the test data set, while SegFormer-B5 outperformed all others with 89.2% of mIoU and 94.6% of MPA on the catfish images. The inference speed for SegFormer-B5 was 0.278 sec per image at the resolution of 640x640. The proposed deep learning-based sensing system is expected to be a reliable tool for automating the catfish cutting process.

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Section: Introductionmentioning

confidence: 99%

Automating catfish cutting process using deep learning-based semantic segmentation

Thayananthan

Zhang

Liu

et al. 2023

Sensing for Agriculture and Food Quality and Safety XV

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“…For fish texture phenotypic features, Maurya et al proposed a color texture feature extraction method based on genetic optimization and a method based on transfer learning to efficiently identify fish color and texture features [17]. In addition, in the actual aquaculture production process, Banwari et al used computer vision to predict the freshness of fish by extracting phenotypic features of fish eyes [18]. Liao et al developed 3DShenoFish software based on deep learning to address the issue of automatic measurement of morphological features, extracting the morphological phenotype of fish from 3D point cloud data [19].…”

Section: Introductionmentioning

confidence: 99%

An Automatic Detection and Counting Method for Fish Lateral Line Scales of Underwater Fish Based on Improved YOLOv5

Yu,

Wang,

Qin

et al. 2023

IEEE Access

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The lateral line scales of fish are an important phenotype of fish species. As an important countable feature, the accurate and effective counting of lateral line scales is an important reference standard for breeding, determining the growth status of fish, and identifying fish species. At present, the statistical work of fish lateral line scales mainly depends on manual statistics and semi-automatic methods, which cannot meet the current needs of sustainable development and precision digital fisheries. The method based on computer vision and deep learning can provide an a real-time, efficient and non-contact method for identifying and counting fish lateral line scales. However, it is still a challenge due to the high similarity between fish scales and the variable size issues caused by the free movement of the fish. Hence, we proposed a transformer module improved YOLOv5 model (TRH-YOLOv5) for fish lateral line scale detection and counting, which focus on the high similarity of fish scales. In addition, we design a small target detection module in the head layer to address the challenge of multi-scale fish. To evaluate the effective of our method, performance of proposed model is analyzed on different type fish dataset and it is also compared with classical method including SSD, YOLOv4 and YOLOv5. Comprehensive experimental results show that the proposed model achieves fine results (e.g., 98.8% precision, 96.7% recall and 99.0% mean average precision) with relatively lower computational coat (e.g., 16.1M model size) and fast detection speed (e.g., 37 FPS) compared with the benchmark algorithm. The TRH-YOLOv5 model is also used for swimming fish video to detect fish lateral line in real-time and can be integrated into aquaculture vision system for aquaculture precision and sustainable management.

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An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness

Bajpai,

Rai,

Tiwari

2023

Lecture Notes in Networks and Systems

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Computer vision technique for freshness estimation from segmented eye of fish image

Cited by 29 publications

References 32 publications

Automating catfish cutting process using deep learning-based semantic segmentation

Automating catfish cutting process using deep learning-based semantic segmentation

An Automatic Detection and Counting Method for Fish Lateral Line Scales of Underwater Fish Based on Improved YOLOv5

An Efficient CNN-based Method for Classification of Red Meat Based on its Freshness

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