Dairy goat detection based on Faster R-CNN from surveillance video

Wang, Dong; Tang, Jian; Zhu, Weijie; Li, Huan; Xin, Jing; He, Dongjian

doi:10.1016/j.compag.2018.09.030

Cited by 53 publications

(28 citation statements)

References 19 publications

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“…Some image processing algorithms were available to compare the differences between adjacent frames or between background frames and frames to be tested and capable of automatically ruling out unnecessary files. These included, but were not limited to, Adaptive Gaussian Mixture Model [ 111 ], Structural Similarity Index Model [ 49 , 70 ], Image Subtraction [ 54 ], K-Mean Clustering [ 97 ], Absolute Histogram Difference-Based Approach [ 112 ], and Dynamic Delaunay Graph Clustering Algorithm [ 65 ].…”

Section: Preparationsmentioning

confidence: 99%

See 1 more Smart Citation

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Huang

Chen

et al. 2021

Sensors

102

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Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.

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Section: Preparationsmentioning

confidence: 99%

“…Alternatively, researchers may have trials of CNN techniques in relatively simple conditions for cattle and pigs. Sheep/goat are generally reared in pasture, where cameras may not be able to cover every sheep/goat [ 65 ], which is an obstacle to develop CNN-based computer vision systems for sheep/goat production.…”

Section: Applicationsmentioning

confidence: 99%

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Huang

Chen

et al. 2021

Sensors

102

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“…For brown and white egg detection, the RMSE results show that the deviation of the predicted egg center from the actual egg center increased at larger camera heights and tilting angles. Table 5 shows the precision, recall, accuracy, and RMSEs of the faster R-CNN detector for floor egg detection under different light intensities (1,5,10,15, and 20 lux) and litter conditions (w/or w/o feather presence) using the five folds of the validation sets. The detector generally performed greatly at most light intensities; however, the recall and accuracy of the detector for brown egg detection were poor (less than 35%) at the 1-lux light intensity.…”

Section: Generalizability Of the Optimal Cnn Floor-egg Detectormentioning

confidence: 99%

Evaluating Convolutional Neural Networks for Cage-Free Floor Egg Detection

Zhao

et al. 2020

Sensors

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The manual collection of eggs laid on the floor (or ‘floor eggs’) in cage-free (CF) laying hen housing is strenuous and time-consuming. Using robots for automatic floor egg collection offers a novel solution to reduce labor yet relies on robust egg detection systems. This study sought to develop vision-based floor-egg detectors using three Convolutional Neural Networks (CNNs), i.e., single shot detector (SSD), faster region-based CNN (faster R-CNN), and region-based fully convolutional network (R-FCN), and evaluate their performance on floor egg detection under simulated CF environments. The results show that the SSD detector had the highest precision (99.9 ± 0.1%) and fastest processing speed (125.1 ± 2.7 ms·image−1) but the lowest recall (72.1 ± 7.2%) and accuracy (72.0 ± 7.2%) among the three floor-egg detectors. The R-FCN detector had the slowest processing speed (243.2 ± 1.0 ms·image−1) and the lowest precision (93.3 ± 2.4%). The faster R-CNN detector had the best performance in floor egg detection with the highest recall (98.4 ± 0.4%) and accuracy (98.1 ± 0.3%), and a medium prevision (99.7 ± 0.2%) and image processing speed (201.5 ± 2.3 ms·image−1); thus, the faster R-CNN detector was selected as the optimal model. The faster R-CNN detector performed almost perfectly for floor egg detection under a wide range of simulated CF environments and system settings, except for brown egg detection at 1 lux light intensity. When tested under random settings, the faster R-CNN detector had 91.9–94.7% precision, 99.8–100.0% recall, and 91.9–94.5% accuracy for floor egg detection. It is concluded that a properly-trained CNN floor-egg detector may accurately detect floor eggs under CF housing environments and has the potential to serve as a crucial vision-based component for robotic floor egg collection systems.

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“…Human activity acknowledgment researches comparable to our work have actually appeared in the latest opportunities. As an example, [4] provided an in-depth survey of human activity appreciation procedures set up coming from 2008 to 2012. Those specific action recognition approaches were actually divided right into 3 different degrees: private discovery (low-level objective), individual monitoring (intermediate-level vision), and also actions understanding strategies (top-notch idea).…”

Section: Introductionmentioning

confidence: 99%

Designing a Deep Neural Networks Structure to Acquire the Top Level Depiction of Human Interactions

Rani,

Naidu,

Shree

2019

IJEAT

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The latest research studies have really concentrated on slim complications including individual action recognition approaches utilizing depth information, 3D-skeleton information, still photo applicable info, spatiotemporal interest point-based approaches, and also human strolling proposition recognition Nevertheless, there has actually really been no methodical research study of individual action recognition. We present the scene context features that describe the atmosphere of the topic worldwide and also local levels. We design a deep neural network structure to acquire the top-level depiction of human activity combining both activity attributes as well as context features.

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Dairy goat detection based on Faster R-CNN from surveillance video

Cited by 53 publications

References 19 publications

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Evaluating Convolutional Neural Networks for Cage-Free Floor Egg Detection

Designing a Deep Neural Networks Structure to Acquire the Top Level Depiction of Human Interactions

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