Dynamic fry counting based on multi-object tracking and one-stage detection

Zhang, Hanyu; Li, Weiran; Qi, Yanyu; Liu, Haonan; Li, Zhenbo

doi:10.1016/j.compag.2023.107871

Cited by 11 publications

(2 citation statements)

References 18 publications

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“…SORT is an instance association algorithm that uses positional information to associate bounding boxes of the same fish individual across consecutive frames, resulting in fish motion trajectories, as shown in Figure 3. SORT was also widely employed for fish tracking (Wang et al, 2021;Gong et al, 2022;Zhang et al, 2023). In this experiment, YOLOv5 with SORT was employed for multiple object tracking of fish videos recorded, utilizing bounding boxes to represent fish motion trajectories, for subsequent behavioral quantification and analysis.…”

Section: Multiple Fish Trackingmentioning

confidence: 99%

Behavior analysis of juvenile steelhead trout under blue and red light color conditions based on multiple object tracking

Li,

Chen,

Huang

et al. 2024

Front. Mar. Sci.

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IntroductionThe lighting environment significantly influences fish behavior. This study explores the impact of diverse lighting conditions on the behavior of steelhead trout (Oncorhynchus mykiss) to illuminate the mechanisms underlying their behavioral responses.MethodsThis experiment was set up with six treatments at a constant light intensity of 150 lx: 12h white light + 12h dark (12 W), 12h blue light + 12h dark (12B), 12h red light + 12h dark (12 R), 1.5h blue light + 9h red light + 1.5h blue light + 12h dark (3B9R), 3h blue light + 6h red light + 3h blue light + 12h dark (6B6R), total 12h of blue and red light + 12h dark (T12BR). A multiple object tracking method, YOLOv5 with SORT, was employed to capture the movement trajectory of each fish, quantifying three motion metrics: swimming velocity, swimming angular velocity, and generalized intersection over union.ResultsThe results revealed that fish exposed to 12R light environment showed significantly higher activity levels than other groups. The mixed light environments (3B9R, 6B6R) formed significant differences in behavioral metrics with 12R earlier than pure light environments (12B, 12W, T12BR), indicating sudden light color changes should be avoided. Fish in the 3B9R environment exhibited the lowest activity level but highest growth performance, with the highest specific growth rate of 1.91±0.12 d-1, a value significantly surpassing the lowest recorded rate, supported by a p-value of 0.0054, indicating it is suitable for steelhead trout cultivation.DiscussBehavioral significant differences were observed as early as week eight, much earlier than physiological differences, which became apparent by week 16. Overall, this paper employs computer vision methods to study the impact of different light colors on fish behavior, found that 3B9R is the optimal lighting condition tested and sudden light color changes should be avoided, offering a new perspective on light conditions and behavior in steelhead trout cultivation.

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Section: Multiple Fish Trackingmentioning

confidence: 99%

Behavior analysis of juvenile steelhead trout under blue and red light color conditions based on multiple object tracking

Li,

Chen,

Huang

et al. 2024

Front. Mar. Sci.

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“…Feng [12] attempted to solve the problems of overlapping, as well as sticking fish fry in water, and proposed a lightweight object detection counting method (YOLOv4-Tiny) based on deep learning and added an attention mechanism (CBAM), which could satisfy edge computing devices to perform automatic counting while obtaining high counting accuracy. Zhang [13] proposed a dynamic fish fry counting method to compensate for the shortcomings of the current methods, which are all implemented in static scenarios. They regarded fish fry counting as a multi-object tracking problem based on tracking by detection, combined YOLOv5 with SORT, and improved the SORT algorithm based on multi-matching and trajectory recovery, for which the final tracking accuracy reached 82.6%.…”

Section: Introductionmentioning

confidence: 99%

Automatic Shrimp Fry Counting Method Using Multi-Scale Attention Fusion

Peng,

Zhou,

Zhang

et al. 2024

Sensors

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Shrimp fry counting is an important task for biomass estimation in aquaculture. Accurate counting of the number of shrimp fry in tanks can not only assess the production of mature shrimp but also assess the density of shrimp fry in the tanks, which is very helpful for the subsequent growth status, transportation management, and yield assessment. However, traditional manual counting methods are often inefficient and prone to counting errors; a more efficient and accurate method for shrimp fry counting is urgently needed. In this paper, we first collected and labeled the images of shrimp fry in breeding tanks according to the constructed experimental environment and generated corresponding density maps using the Gaussian kernel function. Then, we proposed a multi-scale attention fusion-based shrimp fry counting network called the SFCNet. Experiments showed that our proposed SFCNet model reached the optimal performance in terms of shrimp fry counting compared to CNN-based baseline counting models, with MAEs and RMSEs of 3.96 and 4.682, respectively. This approach was able to effectively calculate the number of shrimp fry and provided a better solution for accurately calculating the number of shrimp fry.

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DYOLO: A Novel Object Detection Model for Multi-scene and Multi-object Based on an Improved D-Net Split Task Model is Proposed

Ma,

Bai,

et al. 2024

Lecture Notes in Computer Science

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Dynamic fry counting based on multi-object tracking and one-stage detection

Cited by 11 publications

References 18 publications

Behavior analysis of juvenile steelhead trout under blue and red light color conditions based on multiple object tracking

Behavior analysis of juvenile steelhead trout under blue and red light color conditions based on multiple object tracking

Automatic Shrimp Fry Counting Method Using Multi-Scale Attention Fusion

DYOLO: A Novel Object Detection Model for Multi-scene and Multi-object Based on an Improved D-Net Split Task Model is Proposed

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