Image recognition of individual cow based on SIFT in Lαβ color space

Liu, Feng; Zhang, Chunmei; Lv, Changwei

doi:10.1051/matecconf/201817601023

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Moreover, in our previous research [32], we proposed a cow identification method based on fusion of deep parts features of the cow's side. The method achieved an identification accuracy of 98.36% in a dataset containing 93 cows, which is 0.03% higher than the work of [23]. The proposed cow rump identification method achieved 4.46% and 2.04% improvements in performance compared to the work of using the face and body, respectively, which showed the advantages of high accuracy.…”

Section: Experimental Results and Analysismentioning

confidence: 81%

“…Furthermore, their method was only evaluated on six cows, and as the amount of experimental data increased, the identification accuracy will be affected. The authors of [23] proposed a SIFT-based method to identify the cow's side, which also achieved an identification accuracy of 98.33%. However, the SIFT-based traditional method is greatly affected by the environment, and its calculation amount is large and it is time-consuming, so it is difficult to realize real-time identification in actual production environments.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…However, in a large herd of cows, extracting only the body image of the side of the cow is not enough to ensure the accuracy of identifying the cow. The authors of [23] proposed a cow identification method based on the L component of Lαβ color space to identify the cow's side. In [24], the authors proposed a cow identification method based on three-dimensional video analysis using RGB-D cameras.…”

Section: Introductionmentioning

confidence: 99%

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Cow Rump Identification Based on Lightweight Convolutional Neural Networks

et al. 2021

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Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device.

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Section: Experimental Results and Analysismentioning

confidence: 81%

Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cow Rump Identification Based on Lightweight Convolutional Neural Networks

et al. 2021

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“…Although not primarily a location-determining technique, by mimicking the human eye, computer vision can identify individuals and thus identify an animal's location. Biometric identification of animals through computer vision is possible [148][149][150] and also the tracking and counting of animals [151][152][153][154][155]. The use of a thermal sensor might even improve the tracking capabilities since it will make it easier to detect animals against the background and distinguish between overlapping individuals [156].…”

Section: Computer Vision Technologymentioning

confidence: 99%

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

Hofstra

Roelofs

Rutter

et al. 2022

Dairy

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Several studies have suggested that precision livestock farming (PLF) is a useful tool for animal welfare management and assessment. Location, posture and movement of an individual are key elements in identifying the animal and recording its behaviour. Currently, multiple technologies are available for automated monitoring of the location of individual animals, ranging from Global Navigation Satellite Systems (GNSS) to ultra-wideband (UWB), RFID, wireless sensor networks (WSN) and even computer vision. These techniques and developments all yield potential to manage and assess animal welfare, but also have their constraints, such as range and accuracy. Combining sensors such as accelerometers with any location determining technique into a sensor fusion system can give more detailed information on the individual cow, achieving an even more reliable and accurate indication of animal welfare. We conclude that location systems are a promising approach to determining animal welfare, especially when applied in conjunction with additional sensors, but additional research focused on the use of technology in animal welfare monitoring is needed.

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A study on the application of artificial intelligence in the design of intelligent medical robots

Miao,

Chen,

Zheng

2023

Applied Mathematics and Nonlinear Sciences

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With the development of artificial intelligence and robotics technology, the combination of artificial intelligence and medical device research and development has been promoted, which is an important product of the development of artificial intelligence. In this paper, the general structure of the intelligent medical robot is designed by combining artificial intelligence technology and robotics-related technology. Then, the binocular vision function of the robot was realized by visually acquiring the image of the target object, 3D reconstruction of the target object, and combining the SIFT image recognition algorithm and target tracking algorithm. Then, a new speech recognition algorithm was constructed to realize the human-robot interaction function with the medical robot based on the deep learning Transforme network after the construction of the human acoustic model. Finally, the designed intelligent medical robot was tested, and its overall performance was evaluated. The results show that the recognition errors of the intelligent medical robot on the features of the items are all within 0.05, the recognition errors on the features of the human body are within 0.2, and the speed of the target tracking is between 6km/h and 16km/h. The average recognition accuracy of the medical robot for voice commands is about 0.9, the recognition time is about 0.7s, the normal working rate of each function is more than 0.99, and the test speed is within 2s.

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Image recognition of individual cow based on SIFT in Lαβ color space

Cited by 3 publications

References 6 publications

Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Cow Rump Identification Based on Lightweight Convolutional Neural Networks

Mapping Welfare: Location Determining Techniques and Their Potential for Managing Cattle Welfare—A Review

A study on the application of artificial intelligence in the design of intelligent medical robots

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