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Water conservancy personnel usually need to know the water level by water gauge images in real-time and with an expected accuracy. However, accurately recognizing the water level from water gauge images is still a complex problem. This article proposes a composite method applied in the Wuyuan City, Jiangxi Province, in China. This method can detect water gauge areas and number areas from complex and changeable scenes, accurately detect the water level line from various water gauges, and finally, obtain the accurate water level value. Firstly, FCOS is improved by fusing a contextual adjustment module to meet the requirements of edge computing and ensure considerable detection accuracy. Secondly, to deal with scenes with indistinct water level features, we also apply the contextual adjustment module for Deeplabv3+ to segment the water gauge area above the water surface. Then, the area can be used to obtain the position of the water level line. Finally, the results of the previous two steps are combined to calculate the water level value. Detailed experiments prove that this method solves the problem of water level recognition in complex hydrological scenes. Furthermore, the recognition error of the water level by this method is less than 1 cm, proving it is capable of being applied in real river scenes. [ABSTRACT FROM AUTHOR]

Monitoring pig drinking has been a topic of interest to pig researchers and producers for many years. However, challenges still remain due to the fact that pigs like to play with drinkers in nursery environments and that the drinking pig is often touching others. These factors negatively influence the performance of camera-based pig drinking detection algorithms. The aim of this study is to investigate a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to classify drinking and drinker-playing. In the experiment, two pens of pigs were video recorded for 3 days. In video from the first pen, 5400 2 s drinking episodes and 5400 2 s drinker-playing episodes were generated with 80% of these data being allocated as training set and the remaining 20% as validation set. In video from the second pen, 12,000 2 s drinking and drinker-playing episodes were generated as a test set. Firstly, the CNN architecture ResNet50 was used to extract spatial features. These features were input into LSTM framework to extract spatial–temporal features. Through the fully connected layer, the prediction function Softmax was finally used to classify these drinking and drinker-playing episodes. In the test set, the classification accuracy in the body and head regions of interest was 87.2% and 92.5%, respectively. The results indicate that the proposed method can be used to classify pigs' drinking and drinker-playing. These classification results have potential to improve the accuracy of pig drinking detection and help farmers to estimate pig welfare. • A novel video-based deep learning method was used to study pig drinking behaviour. • Long short-term memory was used to extract the spatial–temporal features. • Classification accuracy in the body and head regions of interest was 87.2% and 92.5%. • Drinking behaviour can be detected under conditions of pigs touching and overlapping. • Shortening the region of interest can be used to improve the classification accuracy. [ABSTRACT FROM AUTHOR]

• Computer vision methods for livestock behaviour recognition were evaluated. • Journey from computer vision to deep learning was elaborated. • This paper involves with segmentation, identification and behaviour recognition. • Research trend of livestock behaviour recognition was illustrated from four aspects. • This review could provide researchers and producers with technical references. The increasing demand for sustainable livestock products also demands new considerations in animal breeding. Breeding programs are now seeking to integrate animal behavioural phenotypes, as these relate to the productivity, health and welfare of the animals and thereby can influence yield and economic benefits in the industry. Traditional manual observation of pig behaviour is time-consuming, laborious, subjective, and difficult to achieve in continuous and large-scale operations. It is not surprising that computer vision technology with the advantages of being objective, non-invasive and continuous has been widely researched for its use in the recognition of livestock behaviours over recent years. Nevertheless, in studies of livestock behaviour recognition, computer vision technology faces some challenges, e.g., complex scenes, variable illumination, occlusion, touching and overlapping between livestock, which has limited the fast translation of technology to industry. On the other hand, deep learning technology has proven to solve these difficulties to a certain extent and is being adopted to recognise livestock behaviours. This paper mainly evaluates the recent developments in computer vision methods for recognition of these behaviours in pigs and cattle. The focus on these species is made possible by the number of studies exist quantifying behaviours that are of importance for their health, welfare and productivity such as aggression, drinking, feeding, lameness, mounting, posture, tail-biting and nursing. This review paper especially analyses the development of image segmentation, identification and behaviour recognition using tradition computer vision and more recent deep learning methods, and evaluates the evolution of key research in the field. We elaborate the research trend of livestock behaviour recognition from four aspects, i.e., development of robust livestock identification algorithms, recognition of livestock behaviours for different growth stages, further quantification of the results of behaviour recognition, and building evaluation system of growth status, health and welfare. [ABSTRACT FROM AUTHOR]

• Feeding behaviour of multiple pigs at the nursery stage was recognised in this study. • Xception and LSTM were combined to extract discriminative spatial-temporal features. • These features can used to recognise feeding behaviour with an accuracy of 98.4%. • The identity and feeding time of each pig were determined in 4 feeding sub-regions. • The proposed algorithm could recognise 98.5% of feeding time of individual pigs. Monitoring the feeding behaviour of pigs and measuring their feeding time can help farmers to evaluate the pig health and welfare. The aim of this study was to develop a video-based deep learning algorithm to recognise feeding behaviour of nursery pigs and determine the feeding time of pigs on an individual level. In the experiment, two pens of pigs were video recorded for 3 days. In the video of feeding region of pen 1, 96,000 1 s feeding episodes and 96,000 1 s non-feeding episodes were generated. 70% of these data was randomly selected as training set and the remaining 30% as validation set. In the video of feeding region of pen 2, all the labelled 24,656 1 s feeding episodes and 61,744 1 s non-feeding episodes were used as test set. Firstly, the Convolutional Neural Network (CNN) architecture Xception was used to extract spatial features. These features were input into Long Short-term Memory (LSTM) framework to extract spatial-temporal features. Through the fully connected layer, the prediction function Softmax was finally used to classify these 1 s episodes as feeding or non-feeding. An image processing algorithm based on maximum entropy segmentation, HSV (Hue, Saturation and Value) colour space transformation and template matching was proposed to calculate the circularity of the head, the ratio of the head to the feeding sub-region, the accumulated pixels of the head motion, and the distance from the head to the number on pig back in order to determine the identity and feeding time of each pig. In the test set, the proposed algorithm could recognise feeding behaviour with an accuracy of 98.4%, a sensitivity of 98.8%, specificity of 98.3% and precision of 95.9%, and could correctly recognise 98.5% of feeding time of individual pigs from the total 45944 s feeding time of 8 pigs. The results indicate that the proposed method can be used to recognise feeding behavior of pigs and determine feeding time of each pig. [ABSTRACT FROM AUTHOR]

• A deep learning method based on CNN and LSTM was used to recognise pig aggression. • This study directly process video episodes rather than individual frames. • CNN features were input into LSTM framework to extract spatial-temporal features. • The proposed method could recognise aggressive episodes with an accuracy of 97.2%. • A frame skipping approach can improve the accuracy to 98.4% and halve running time. Aggression is considered as a major animal welfare problem in commercial pig farming. The aim of this study is to develop a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to recognise aggressive episodes of pigs. Compared to previous studies of pig behaviours based on deep learning, this study directly process video episodes rather than individual frames. In the experiment, nursery pigs (8/pen) were mixed for 3 days and then 8 h of video was recorded in each day. From these videos, 600 aggressive 2 s-episodes were manually selected and then augmented into 2400 episodes by using horizontal, vertical and diagonal mirroring. From the videos, 2400 non-aggressive 2 s-episodes were also manually selected. 80% of the data were randomly allocated as training set and the remaining 20% as validation set. Firstly, the CNN architecture VGG-16 was used to extract spatial features. These features were then input into LSTM framework to further extract temporal features. Through fully connected layer, the prediction function Softmax was finally used to determine if the current episode is aggression or non-aggression. Using the proposed method, aggressive episodes could be recognised with an accuracy of 97.2%. This result indicates that this method can be used to recognise aggressive episodes of pigs. Additionally, this paper further investigates the validity of this method under the conditions of skipping frames and reducing the episode length. The results show that a frame skipping approach whereby 30 fps is reduced into 15 fps within each 2 s-episode can improve the accuracy into 98.4% and halve the total running time. [ABSTRACT FROM AUTHOR]

• Computer vision was developed to classify pig agonistic behaviors in feeding stalls. • This study utilized different validation strategies to evaluate model performance. • Random validation showed best performance but not comparable to real-world practices. Agonistic behavior at feeding spaces is associated with both welfare and feed intake issues in swine farming. Studying interactive social behaviors of group-housed pigs provides valuable information to improve their production and welfare. The aims of this study were to (1) develop a deep learning pipeline based on convolutional neural network (CNN) and long short-term memory (LSTM) to classify videos depicting four types of interactive behavior between pigs in a single-space feeding stall and (2) validate the CNN + LSTM pipeline through various blocked validation strategies. Four categories of behaviors were classified in this study: head-to-body contact (including gentle nosing, casual contact between head/ears of a pig with a feeding pig, head knocking, tail biting, and pushing); levering where the feeding pig was lifted from behind by another pig; mounting in which the feeding pig was mounted by another pig; and no-contact when a second pig entered the feeding stall without physical contact with the feeding pig. Behavior at the feeding stall was filmed twice, three weeks apart, for two consecutive days each week using six groups of grow-finish pigs (10 per group) housed in pens equipped with FIRE® feeders. This resulted in a total of 15,679 30-frame video episodes for classification. Random validation, blocking-by-time validation, and blocking-by-feeder validation were utilized for training-validation data split. The size of training sets was held constant (N = 7,500) through all validation scenarios. The average validation accuracies were 0.968(±0.001), 0.860(±0.033), 0.766(±0.026), and 0.860(±0.010) for random validation, blocking-by-time validation, and blocking-by-feeder validation (at Feeder 1 and at Feeder 2), respectively. The results indicate that the CNN + LSTM pipeline yielded acceptable predictive performance in random validation. However, performance was substantially worse in blocking-by-time and blocking-by-feeder validations. More work is needed for algorithm generalization to improve its robustness across a variety of real-life application scenarios. We provide public access to the dataset and the code. [ABSTRACT FROM AUTHOR]

New processing methods based on artificial intelligence (AI) and deep learning are replacing traditional computer vision algorithms. The more advanced systems can process huge amounts of data in large computing facilities. In contrast, this paper presents a smart video surveillance system executing AI algorithms in low power consumption embedded devices. The computer vision algorithm, typical for surveillance applications, aims to detect, count and track people's movements in the area. This application requires a distributed smart camera system. The proposed AI application allows detecting people in the surveillance area using a MobileNet-SSD architecture. In addition, using a robust Kalman filter bank, the algorithm can keep track of people in the video also providing people counting information. The detection results are excellent considering the constraints imposed on the process. The selected architecture for the edge node is based on a UpSquared2 device that includes a vision processor unit (VPU) capable of accelerating the AI CNN inference. The results section provides information about the image processing time when multiple video cameras are connected to the same edge node, people detection precision and recall curves, and the energy consumption of the system. The discussion of results shows the usefulness of deploying this smart camera node throughout a distributed surveillance system. [ABSTRACT FROM AUTHOR]