Automatic Monitoring of Chicken Movement and Drinking Time Using Convolutional Neural Networks

2020 ◽  
Vol 63 (6) ◽  
pp. 2029-2038
Author(s):  
Chen-Yi Lin ◽  
Kuang-Wen Hsieh ◽  
Yao-Chuan Tsai ◽  
Yan-Fu Kuo
Keyword(s):  
Neural Network ◽  
Heat Stress ◽  
Embedded System ◽  
Convolutional Neural Network ◽  
Time Lapse ◽  
Detection Accuracy ◽  
List Type ◽  
Tracking Accuracy ◽  
Temperature Humidity Index ◽  
Humidity Index

HighlightsA customized embedded system was built to acquire images of a chicken coop.Faster R-CNN was used to localize the chickens in the images.The accuracies in chicken detection and tracking were 98.16% and 98.94%, respectively.Movement and drinking time of chickens were quantified.Abstract. Poultry and eggs are major sources of dietary protein worldwide. Because Taiwan is located in tropical and subtropical regions, heat stress in chickens is one of the most challenging concerns of the poultry industry in Taiwan. Typical heat stress symptoms in chickens are reduced movement and increased drinking time. The level of heat stress is conventionally evaluated using the temperature-humidity index (THI) or through manual observation. However, THI is indirect, and manual observation is subjective and time-consuming. This study proposes to directly monitor the movement and drinking time of chickens using time-lapse images and deep learning algorithms. In this study, an experimental coop was constructed to house ten chickens. An embedded system was then designed to acquire images of the chickens at a rate of 1 frame s-1 and to measure the temperature and humidity of the coop. A faster region-based convolutional neural network was then trained on a personal computer to detect and localize the chickens in the images. The movement and drinking time of the chickens under various THI values were then analyzed. The proposed method provided 98.16% chicken detection accuracy and 98.94% chicken tracking accuracy. Keywords: Chicken activities, Embedded system, Faster region-based convolutional neural network, Faster R-CNN, Heat stress, Temperature-humidity index (THI).

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

scholarly journals RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

2021 ◽  
pp. 55-55
Author(s):  
Benhui Xia ◽  
Dezhi Han ◽  
Ximing Yin ◽  
Gao Na
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Recognition Rate ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Traffic Classification ◽  
High Detection Rate ◽  
Network Intrusion

To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct map ping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.

scholarly journals A Single Target Grasp Detection Network Based on Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Longzhi Zhang ◽  
Dongmei Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
High Accuracy ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Single Object ◽  
Single Target ◽  
High Detection ◽  
Good Detection

Grasp detection based on convolutional neural network has gained some achievements. However, overfitting of multilayer convolutional neural network still exists and leads to poor detection precision. To acquire high detection accuracy, a single target grasp detection network that generalizes the fitting of angle and position, based on the convolution neural network, is put forward here. The proposed network regards the image as input and grasping parameters including angle and position as output, with the detection manner of end-to-end. Particularly, preprocessing dataset is to achieve the full coverage to input of model and transfer learning is to avoid overfitting of network. Importantly, a series of experimental results indicate that, for single object grasping, our network has good detection results and high accuracy, which proves that the proposed network has strong generalization in direction and category.

scholarly journals Critical Temperature-Humidity Index Thresholds for Dry Cows in a Subtropical Climate

2021 ◽  
Vol 2 ◽  
Author(s):  
Véronique Ouellet ◽  
Izabella M. Toledo ◽  
Bethany Dado-Senn ◽  
Geoffrey E. Dahl ◽  
Jimena Laporta
Keyword(s):  
Heat Stress ◽  
Respiration Rate ◽  
Rectal Temperature ◽  
Dry Matter ◽  
Dry Matter Intake ◽  
Subtropical Climate ◽  
Dry Period ◽  
Environmental Indices ◽  
Temperature Humidity Index ◽  
Humidity Index

The effects of heat stress on dry cows are profound and significantly contribute to lower overall welfare, productivity, and profitability of the dairy sector. Although dry cows are more thermotolerant than lactating cows due to their non-lactating state, similar environmental thresholds are currently used to estimate the degree of heat strain and cooling requirements. Records of dry cow studies conducted over 5 years in Gainesville, Florida, USA were pooled and analyzed to determine environmental thresholds at which dry cows exhibit signs of heat stress in a subtropical climate. Dry-pregnant multiparous dams were actively cooled (CL; shade of a freestall barn, fans and water soakers, n = 107) or not (HT; shade only, n = 111) during the last 7 weeks of gestation, concurrent with the entire dry period. Heat stress environmental indices, including ambient temperature, relative humidity, and temperature-humidity index (THI), and animal-based indices, including respiration rate, rectal temperature and daily dry matter intake were recorded in all studies. Simple correlations were performed between temperature-humidity index and each animal-based indicator. Differences in respiration rate, rectal temperature and dry matter intake between treatments were analyzed by multiple regression. Using segmented regression, temperature-humidity thresholds for significant changes in animal-based indicators of heat stress were estimated. Stronger significant correlations were found between the temperature-humidity index and all animal-based indices measured in HT dry cows (−0.22 ≤ r ≤ 0.35) relative to CL dry cows (−0.13 ≤ r ≤ 0.19). Although exposed to similar temperature-humidity index, rectal temperature (+0.3°C; P < 0.001) and respiration rate (+23 breaths/min; P < 0.001) were elevated in HT dry cows compared with CL cows whereas dry matter intake (−0.4 kg of dry matter/d; P = 0.003) was reduced. Temperature-humidity index thresholds at which respiration rate and rectal temperature began to change were both determined at a THI of 77 in HT dry cows. No significant temperature-humidity threshold was detected for dry matter intake. At a practical level, our results demonstrate that dry cow respiration rate and rectal temperature increased abruptly at a THI of 77 when provided only shade and managed in a subtropical climate. Therefore, in the absence of active cooling, dry cows should be closely monitored when or before THI reaches 77 to avoid further heat-stress related impairments during the dry period and the subsequent lactation and to mitigate potential carry-over effects on the offspring.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

scholarly journals A Deep Learning-Based Approach for the Detection of Early Signs of Gingivitis in Orthodontic Patients Using Faster Region-Based Convolutional Neural Networks

2020 ◽  
Vol 17 (22) ◽  
pp. 8447
Author(s):  
Dima M. Alalharith ◽  
Hajar M. Alharthi ◽  
Wejdan M. Alghamdi ◽  
Yasmine M. Alsenbel ◽  
Nida Aslam ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Periodontal Disease ◽  
Convolutional Neural Network ◽  
Region Of Interest ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Detection Model ◽  
Art Object ◽  
Orthodontic Patients

Computer-based technologies play a central role in the dentistry field, as they present many methods for diagnosing and detecting various diseases, such as periodontitis. The current study aimed to develop and evaluate the state-of-the-art object detection and recognition techniques and deep learning algorithms for the automatic detection of periodontal disease in orthodontic patients using intraoral images. In this study, a total of 134 intraoral images were divided into a training dataset (n = 107 [80%]) and a test dataset (n = 27 [20%]). Two Faster Region-based Convolutional Neural Network (R-CNN) models using ResNet-50 Convolutional Neural Network (CNN) were developed. The first model detects the teeth to locate the region of interest (ROI), while the second model detects gingival inflammation. The detection accuracy, precision, recall, and mean average precision (mAP) were calculated to verify the significance of the proposed model. The teeth detection model achieved an accuracy, precision, recall, and mAP of 100 %, 100%, 51.85%, and 100%, respectively. The inflammation detection model achieved an accuracy, precision, recall, and mAP of 77.12%, 88.02%, 41.75%, and 68.19%, respectively. This study proved the viability of deep learning models for the detection and diagnosis of gingivitis in intraoral images. Hence, this highlights its potential usability in the field of dentistry and aiding in reducing the severity of periodontal disease globally through preemptive non-invasive diagnosis.

scholarly journals Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network

2020 ◽  
Vol 10 (14) ◽  
pp. 4720 ◽  
Author(s):  
Zhiqiang Teng ◽  
Shuai Teng ◽  
Jiqiao Zhang ◽  
Gongfa Chen ◽  
Fangsen Cui
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Structural State ◽  
Detection Method ◽  
Steel Frame ◽  
Engineering Practice ◽  
Detection Accuracy ◽  
Vibration Signals

The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice.

In Situ Motor Fault Diagnosis Using Enhanced Convolutional Neural Network in an Embedded System

2020 ◽  
Vol 20 (15) ◽  
pp. 8287-8296 ◽  
Author(s):  
Siliang Lu ◽  
Gang Qian ◽  
Qingbo He ◽  
Fang Liu ◽  
Yongbin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Embedded System ◽  
Convolutional Neural Network

scholarly journals Kenaf plant pest and disease detection using faster regional based convolutional neural network

2021 ◽  
Vol 24 (1) ◽  
pp. 198
Author(s):  
Alfita Rakhmandasari ◽  
Wayan Firdaus Mahmudy ◽  
Titiek Yulianti
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Crop Production ◽  
Computation Time ◽  
Raw Material ◽  
Detection Accuracy ◽  
Pulp Fiber ◽  
Pests And Diseases ◽  
Processing Procedure ◽  
Selection Of

<span>Kenaf plant is a fibre plant whose stem bark is taken to be used as raw material for making geo-textile, particleboard, pulp, fiber drain, fiber board, and paper. The presence of plant pests and diseases that attack causes crop production to decrease. The detection of pests and diseases by farmers may be a challenging task. The detection can be done using artificial intelligence-based method. Convolutional neural networks (CNNs) are one of the most popular neural network architectures and have been successfully implemented for image classification. However, the CNN method is still considered a long time in the process, so this method was developed into namely faster regional based convolution neural network (RCNN). As the selection of the input features largely determines the accuracy of the results, a pre-processing procedure is developed to transform the kenaf plant image into input features of faster RCNN. A computational experiment proves that the faster RCNN has a very short computation time by completing 10000 iterations in 3 hours compared to convolutional neural network (CNN) completing 100 iterations at the same time. Furthermore, Faster RCNN gets 77.50% detection accuracy and bounding box accuracy 96.74% while CNN gets 72.96% detection accuracy at 400 epochs. The results also prove that the selection of input features and its pre-processing procedure could produce a high accuracy of detection. </span>

scholarly journals Effect of Temperature-Humidity-Index on Milk Performances of Local Born Holstein Dairy Cows Under Saharan Climate

2021 ◽  
Vol 24 (2) ◽  
pp. 24-36
Author(s):  
Lazoumi Ouarfli ◽  
Abdelmadjid Chehma
Keyword(s):  
Heat Stress ◽  
Dairy Cows ◽  
Environmental Conditions ◽  
Arid Regions ◽  
Effect Of Temperature ◽  
Significant Drop ◽  
Temperature Humidity Index ◽  
Lactation Length ◽  
Humidity Index ◽  
Milking Frequency

Abstract The objective is to study the effect of heat stress on milk yield (MY) relative to milking records (n=18178) of native Holsteins (n=187), in the region of Ghardaia, according to periods of HS, using the temperature-humidity index (THI). With THI >72 during 07 months in the study area, which significantly (P<0.001) decrease the MY (-15.5% corresponding to 21.73 kg). Also, calving periods led to a significant drop (P < 0.001) in overall MY (7030.35 kg) of the order of (-14.6%), and over the lactation length (353.43 d), which explains 41% of the variations in MY. In addition, the non-significant effect (P=0.212) of the lactation range on the increase in MY, moreover, the lactation length shows a non-significant (P = 0.108) decrease (-4.68%) during heat stress (HS). Furthermore, the significant effect (P <0.001) of the interaction (Milking frequency × THI) on MY, when THI variates from < 74 to > 84, with regression of (-16.82% and -08.82%) of the MF (2X and 3X), respectively. Again, the NH cow is less sensitive to hyperthermia, so THI explains only 2% of the variation in MY levels. Thus, NH in arid regions have the ability to acclimatize to Saharan environmental conditions.

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