Image-Based Goat Breed Identification and Localization Using Deep Learning

2020 ◽  
Vol 10 (4) ◽  
pp. 74-96
Author(s):  
Pritam Ghosh ◽  
Subhranil Mustafi ◽  
Satyendra Nath Mandal
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Transfer Learning ◽  
Digital Images ◽  
Evaluation Metrics ◽  
Natural Environments ◽  
Goat Breed ◽  
Pure Breed ◽  
Detection Model ◽  
Detection Evaluation

In this paper an attempt has been made to identify six different goat breeds from pure breed goat images. The images of goat breeds have been captured from different organized registered goat farms in India, and almost two thousand digital images of individual goats were captured in restricted (to get similar image background) and unrestricted (natural) environments without imposing stress to animals. A pre-trained deep learning-based object detection model called Faster R-CNN has been fine-tuned by using transfer-learning on the acquired images for automatic classification and localization of goat breeds. This fine-tuned model is able to locate the goat (localize) and classify (identify) its breed in the image. The Pascal VOC object detection evaluation metrics have been used to evaluate this model. Finally, comparison has been made with prediction accuracies of different technologies used for different animal breed identification.

scholarly journals Temperate fish detection and classification: a deep learning based approach

2021 ◽  
Author(s):  
Kristian Muri Knausgård ◽  
Arne Wiklund ◽  
Tonje Knutsen Sørdalen ◽  
Kim Tallaksen Halvorsen ◽  
Alf Ring Kleiven ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
A Priori ◽  
Training Model ◽  
Marine Ecology ◽  
Natural Environments ◽  
Detection Model ◽  
Wide Range ◽  
Limited Training Samples ◽  
Temperate Fish

AbstractA wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27% using the pre-training model. The accuracies using the post-training model are also high; 83.68% and 87.74% with and without image augmentation, respectively. This strongly indicates that the solution is viable with a more extensive dataset.

scholarly journals Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

2020 ◽  
Vol 13 (1) ◽  
pp. 23
Author(s):  
Wei Zhao ◽  
William Yamada ◽  
Tianxin Li ◽  
Matthew Digman ◽  
Troy Runge
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Precision Agriculture ◽  
Crop Production ◽  
Domain Adaptation ◽  
Training Data ◽  
Detection Accuracy ◽  
Detection Model ◽  
Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

Prediction of Crime Scene Objects Using Deep Learning

2021 ◽  
Vol 9 (VIII) ◽  
pp. 857-864
Author(s):  
Vibhavari B Rao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Computer Architecture ◽  
Real Time ◽  
Surveillance System ◽  
Crime Scene ◽  
Detection Model ◽  
Detection Algorithms ◽  
Performance Metric ◽  
Smart Surveillance

The crime rates today can inevitably put a civilian's life in danger. While consistent efforts are being made to alleviate crime, there is also a dire need to create a smart and proactive surveillance system. Our project implements a smart surveillance system that would alert the authorities in real-time when a crime is being committed. During armed robberies and hostage situations, most often, the police cannot reach the place on time to prevent it from happening, owing to the lag in communication between the informants of the crime scene and the police. We propose an object detection model that implements deep learning algorithms to detect objects of violence such as pistols, knives, rifles from video surveillance footage, and in turn send real-time alerts to the authorities. There are a number of object detection algorithms being developed, each being evaluated under the performance metric mAP. On implementing Faster R-CNN with ResNet 101 architecture we found the mAP score to be about 91%. However, the downside to this is the excessive training and inferencing time it incurs. On the other hand, YOLOv5 architecture resulted in a model that performed very well in terms of speed. Its training speed was found to be 0.012 s / image during training but naturally, the accuracy was not as high as Faster R-CNN. With good computer architecture, it can run at about 40 fps. Thus, there is a tradeoff between speed and accuracy and it's important to strike a balance. We use transfer learning to improve accuracy by training the model on our custom dataset. This project can be deployed on any generic CCTV camera by setting up a live RTSP (real-time streaming protocol) and streaming the footage on a laptop or desktop where the deep learning model is being run.

Deep Learning Based Active Monitoring for Anti-collision between Vessels and Bridges

2019 ◽  
Author(s):  
Limu Chen ◽  
Ye Xia ◽  
Dexiong Pan ◽  
Chengbin Wang
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Data Augmentation ◽  
Information Support ◽  
Single Shot ◽  
Active Monitoring ◽  
Detection Model ◽  
Comparison Results

<p>Deep-learning based navigational object detection is discussed with respect to active monitoring system for anti-collision between vessel and bridge. Motion based object detection method widely used in existing anti-collision monitoring systems is incompetent in dealing with complicated and changeable waterway for its limitations in accuracy, robustness and efficiency. The video surveillance system proposed contains six modules, including image acquisition, detection, tracking, prediction, risk evaluation and decision-making, and the detection module is discussed in detail. A vessel-exclusive dataset with tons of image samples is established for neural network training and a SSD (Single Shot MultiBox Detector) based object detection model with both universality and pertinence is generated attributing to tactics of sample filtering, data augmentation and large-scale optimization, which make it capable of stable and intelligent vessel detection. Comparison results with conventional methods indicate that the proposed deep-learning method shows remarkable advantages in robustness, accuracy, efficiency and intelligence. In-situ test is carried out at Songpu Bridge in Shanghai, and the results illustrate that the method is qualified for long-term monitoring and providing information support for further analysis and decision making.</p>

scholarly journals Improved Method to Detect the Tailings Ponds from Multispectral Remote Sensing Images Based on Faster R-CNN and Transfer Learning

2021 ◽  
Vol 14 (1) ◽  
pp. 103
Author(s):  
Dongchuan Yan ◽  
Hao Zhang ◽  
Guoqing Li ◽  
Xiangqiang Li ◽  
Hua Lei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Object Detection ◽  
Transfer Learning ◽  
Near Infrared ◽  
Image Data ◽  
Remote Sensing Images ◽  
Tailings Pond ◽  
True Color ◽  
Tailings Ponds

The breaching of tailings pond dams may lead to casualties and environmental pollution; therefore, timely and accurate monitoring is an essential aspect of managing such structures and preventing accidents. Remote sensing technology is suitable for the regular extraction and monitoring of tailings pond information. However, traditional remote sensing is inefficient and unsuitable for the frequent extraction of large volumes of highly precise information. Object detection, based on deep learning, provides a solution to this problem. Most remote sensing imagery applications for tailings pond object detection using deep learning are based on computer vision, utilizing the true-color triple-band data of high spatial resolution imagery for information extraction. The advantage of remote sensing image data is their greater number of spectral bands (more than three), providing more abundant spectral information. There is a lack of research on fully harnessing multispectral band information to improve the detection precision of tailings ponds. Accordingly, using a sample dataset of tailings pond satellite images from the Gaofen-1 high-resolution Earth observation satellite, we improved the Faster R-CNN deep learning object detection model by increasing the inputs from three true-color bands to four multispectral bands. Moreover, we used the attention mechanism to recalibrate the input contributions. Subsequently, we used a step-by-step transfer learning method to improve and gradually train our model. The improved model could fully utilize the near-infrared (NIR) band information of the images to improve the precision of tailings pond detection. Compared with that of the three true-color band input models, the tailings pond detection average precision (AP) and recall notably improved in our model, with the AP increasing from 82.3% to 85.9% and recall increasing from 65.4% to 71.9%. This research could serve as a reference for using multispectral band information from remote sensing images in the construction and application of deep learning models.

scholarly journals Automated color detection in orchids using color labels and deep learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0259036
Author(s):  
Diah Harnoni Apriyanti ◽  
Luuk J. Spreeuwers ◽  
Peter J. F. Lucas ◽  
Raymond N. J. Veldhuis
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Recognition System ◽  
Ensemble Classifier ◽  
Ensemble Classifiers ◽  
Detection Model ◽  
Color Detection ◽  
Multi Class Classification ◽  
The One ◽  
Color Scheme

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.

scholarly journals Vehicle Owner Recognition and Speed Estimation through LPD using Deep learning (VORSELD)

2021 ◽  
Vol 40 ◽  
pp. 01005
Author(s):  
Mudit Shrivastava ◽  
Rahul Jadhav ◽  
Pranjal Singhal ◽  
Savita R. Bhosale
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
License Plate ◽  
Speed Estimation ◽  
Moving Vehicle ◽  
Detection Model ◽  
Vehicle Owner ◽  
Made In

As name characterizes understanding of a number plate accordingly, from past decades the use vehicles expanded rapidly, taking into account of this such a majority number of issues like overseeing and controlling trafficante keeping watch on autos and managing parking area zones to overcome this tag recognizer programming is required. The proposed work aims to detect speed of a moving vehicle through its license plate. It will fetch vehicle owner details with the help of CNN model. In this project the main focus is to detect a moving car whenever it crosses dynamic markings. It uses Tensor-flow with an SSD object detection model to detect cars and from the detection in each frame the license plate gets detected and each vehicle can be tracked across a video and can be checked if it crossed the markings made in program itself and hence speed of that vehicle can be calculated. The detected License plate will be forwarded to trained model where PyTesseract is used, which will convert image to text.

scholarly journals Object Detection Model Based on Deep Dilated Convolutional Networks by Fusing Transfer Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 178699-178709
Author(s):  
Yu Quan ◽  
Zhixin Li ◽  
Canlong Zhang ◽  
Huifang Ma
Keyword(s):  
Object Detection ◽  
Transfer Learning ◽  
Detection Model ◽  
Convolutional Networks ◽  
Model Based
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