2020
DOI: 10.3390/s20020332
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Evaluating Convolutional Neural Networks for Cage-Free Floor Egg Detection

Abstract: 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 eva… Show more

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Cited by 22 publications
(12 citation statements)
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“…This unique ability enables automated monitoring systems to offer welfare-centred intervention decisions. This technology also permits the use of robust detection of eggs, which will make the tedious and time-consuming task of floor egg collection easier for farmers [16]. Behavioural issues in group-housed turkeys, such as cannibalism, can be rapidly detected and consequently addressed through deep learning techniques [17].…”
Section: Need For Automated Poultry Surveillancementioning
confidence: 99%
“…This unique ability enables automated monitoring systems to offer welfare-centred intervention decisions. This technology also permits the use of robust detection of eggs, which will make the tedious and time-consuming task of floor egg collection easier for farmers [16]. Behavioural issues in group-housed turkeys, such as cannibalism, can be rapidly detected and consequently addressed through deep learning techniques [17].…”
Section: Need For Automated Poultry Surveillancementioning
confidence: 99%
“…However, the detector may also be trained with other images containing other types of chickens, which can extend application range. Based on our previous experiment, deep learning networks could be generalized to different light intensities, backgrounds, object colors, object numbers and object sizes, as long as they were fed and trained with enough sample images [14]. As shown in Figure 8a, birds typically preened for less than 30 s and some birds even did it shorter (<1 s).…”
Section: Ambiguous Preening Behaviormentioning
confidence: 99%
“…Convolutional neural networks have been widely utilized for object detection in agricultural applications [ 12 , 13 ]. With sufficient training, the CNN detectors could precisely detect objects of concern in various environments [ 14 ]. Meanwhile, the CNN detectors can be integrated into various vision systems to detect objects non-invasively, which is suitable to detect natural behaviors of poultry without extra interferences.…”
Section: Introductionmentioning
confidence: 99%
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“…In addition, there are also multi-spectral systems that use artificial lighting to mitigate the disturbance caused by natural lighting conditions [ 34 , 35 ]. The use of deep learning or machine learning methods can also improve image classification, and this has been applied to estimate the number of livestock [ 36 , 37 ], the number of egg detections [ 38 ], and in other areas [ 39 , 40 ]. The use of these methods can avoid object detection errors or failures caused by poor lighting conditions via traditional image-processing methods.…”
Section: Introductionmentioning
confidence: 99%