scholarly journals SeeCucumbers: Using Deep Learning and Drone Imagery to Detect Sea Cucumbers on Coral Reef Flats

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
Joan Y. Q. Li ◽  
Stephanie Duce ◽  
Karen E. Joyce ◽  
Wei Xiang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Detection Algorithm ◽  
Average Density ◽  
Ecological Impacts ◽  
Detector Performance ◽  
Sea Cucumbers ◽  
Small Areas ◽  
Detection Model ◽  
Optimal Detector

Sea cucumbers (Holothuroidea or holothurians) are a valuable fishery and are also crucial nutrient recyclers, bioturbation agents, and hosts for many biotic associates. Their ecological impacts could be substantial given their high abundance in some reef locations and thus monitoring their populations and spatial distribution is of research interest. Traditional in situ surveys are laborious and only cover small areas but drones offer an opportunity to scale observations more broadly, especially if the holothurians can be automatically detected in drone imagery using deep learning algorithms. We adapted the object detection algorithm YOLOv3 to detect holothurians from drone imagery at Hideaway Bay, Queensland, Australia. We successfully detected 11,462 of 12,956 individuals over 2.7ha with an average density of 0.5 individual/m2. We tested a range of hyperparameters to determine the optimal detector performance and achieved 0.855 mAP, 0.82 precision, 0.83 recall, and 0.82 F1 score. We found as few as ten labelled drone images was sufficient to train an acceptable detection model (0.799 mAP). Our results illustrate the potential of using small, affordable drones with direct implementation of open-source object detection models to survey holothurians and other shallow water sessile species.

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 A Deep Learning-Based Fragment Detection Approach for the Arena Fragmentation Test

2020 ◽  
Vol 10 (14) ◽  
pp. 4744
Author(s):  
Hyukzae Lee ◽  
Jonghee Kim ◽  
Chanho Jung ◽  
Yongchan Park ◽  
Woong Park ◽  
...  
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Object Detection ◽  
High Speed ◽  
Detection Algorithm ◽  
Learning Technologies ◽  
Experimental Conditions ◽  
Fragmentation Test ◽  
Detection Approach ◽  
Previous Image

The arena fragmentation test (AFT) is one of the tests used to design an effective warhead. Conventionally, complex and expensive measuring equipment is used for testing a warhead and measuring important factors such as the size, velocity, and the spatial distribution of fragments where the fragments penetrate steel target plates. In this paper, instead of using specific sensors and equipment, we proposed the use of a deep learning-based object detection algorithm to detect fragments in the AFT. To this end, we acquired many high-speed videos and built an AFT image dataset with bounding boxes of warhead fragments. Our method fine-tuned an existing object detection network named the Faster R-convolutional neural network (CNN) on this dataset with modification of the network’s anchor boxes. We also employed a novel temporal filtering method, which was demonstrated as an effective non-fragment filtering scheme in our recent previous image processing-based fragment detection approach, to capture only the first penetrating fragments from all detected fragments. We showed that the performance of the proposed method was comparable to that of a sensor-based system under the same experimental conditions. We also demonstrated that the use of deep learning technologies in the task of AFT significantly enhanced the performance via a quantitative comparison between our proposed method and our recent previous image processing-based method. In other words, our proposed method outperformed the previous image processing-based method. The proposed method produced outstanding results in terms of finding the exact fragment positions.

scholarly journals Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2484 ◽  
Author(s):  
Weixing Zhang ◽  
Chandi Witharana ◽  
Weidong Li ◽  
Chuanrong Zhang ◽  
Xiaojiang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Measurement Method ◽  
Detection Algorithm ◽  
Quality Data ◽  
Visual Interpretation ◽  
Buffer Zones ◽  
Google Street View ◽  
Street View ◽  
Utility Poles

Traditional methods of detecting and mapping utility poles are inefficient and costly because of the demand for visual interpretation with quality data sources or intense field inspection. The advent of deep learning for object detection provides an opportunity for detecting utility poles from side-view optical images. In this study, we proposed using a deep learning-based method for automatically mapping roadside utility poles with crossarms (UPCs) from Google Street View (GSV) images. The method combines the state-of-the-art DL object detection algorithm (i.e., the RetinaNet object detection algorithm) and a modified brute-force-based line-of-bearing (LOB, a LOB stands for the ray towards the location of the target [UPC at here] from the original location of the sensor [GSV mobile platform]) measurement method to estimate the locations of detected roadside UPCs from GSV. Experimental results indicate that: (1) both the average precision (AP) and the overall accuracy (OA) are around 0.78 when the intersection-over-union (IoU) threshold is greater than 0.3, based on the testing of 500 GSV images with a total number of 937 objects; and (2) around 2.6%, 47%, and 79% of estimated locations of utility poles are within 1 m, 5 m, and 10 m buffer zones, respectively, around the referenced locations of utility poles. In general, this study indicates that even in a complex background, most utility poles can be detected with the use of DL, and the LOB measurement method can estimate the locations of most UPCs.

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

scholarly journals Research on Object Detection Algorithm Based on Deep Learning

2021 ◽  
Vol 1995 (1) ◽  
pp. 012046
Author(s):  
Meian Li ◽  
Haojie Zhu ◽  
Hao Chen ◽  
Lixia Xue ◽  
Tian Gao
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Detection Algorithm

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

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 U-Net-Based Foreign Object Detection Method Using Effective Image Acquisition System: A Case of Almond and Green Onion Flake Food Process

2021 ◽  
Vol 13 (24) ◽  
pp. 13834
Author(s):  
Guk-Jin Son ◽  
Dong-Hoon Kwak ◽  
Mi-Kyung Park ◽  
Young-Duk Kim ◽  
Hee-Chul Jung
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Quality Evaluation ◽  
Detection Algorithm ◽  
Synthetic Dataset ◽  
Foreign Object ◽  
Food Manufacturing ◽  
Real Dataset ◽  
The Real ◽  
Foreign Objects

Supervised deep learning-based foreign object detection algorithms are tedious, costly, and time-consuming because they usually require a large number of training datasets and annotations. These disadvantages make them frequently unsuitable for food quality evaluation and food manufacturing processes. However, the deep learning-based foreign object detection algorithm is an effective method to overcome the disadvantages of conventional foreign object detection methods mainly used in food inspection. For example, color sorter machines cannot detect foreign objects with a color similar to food, and the performance is easily degraded by changes in illuminance. Therefore, to detect foreign objects, we use a deep learning-based foreign object detection algorithm (model). In this paper, we present a synthetic method to efficiently acquire a training dataset of deep learning that can be used for food quality evaluation and food manufacturing processes. Moreover, we perform data augmentation using color jitter on a synthetic dataset and show that this approach significantly improves the illumination invariance features of the model trained on synthetic datasets. The F1-score of the model that trained the synthetic dataset of almonds at 360 lux illumination intensity achieved a performance of 0.82, similar to the F1-score of the model that trained the real dataset. Moreover, the F1-score of the model trained with the real dataset combined with the synthetic dataset achieved better performance than the model trained with the real dataset in the change of illumination. In addition, compared with the traditional method of using color sorter machines to detect foreign objects, the model trained on the synthetic dataset has obvious advantages in accuracy and efficiency. These results indicate that the synthetic dataset not only competes with the real dataset, but they also complement each other.

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