scholarly journals Exploring Underwater Target Detection Algorithm Based on Improved SSD

2020 ◽  
Vol 38 (4) ◽  
pp. 747-754
Author(s):  
Wei Qiang ◽  
Yuyao He ◽  
Yujin Guo ◽  
Baoqi Li ◽  
Lingjiao He
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Algorithm ◽  
Convolution Neural Network ◽  
Detection Accuracy ◽  
Detection Model ◽  
Underwater Environment ◽  
Underwater Target Detection ◽  
Basic Network ◽  
Underwater Target

As the in-depth exploration of oceans continues, the accurate and rapid detection of fish, bionics and other intelligent bodies in an underwater environment is more and more important for improving an underwater defense system. Because of the low accuracy and poor real-time performance of target detection in the complex underwater environment, we propose a target detection algorithm based on the improved SSD. We use the ResNet convolution neural network instead of the VGG convolution neural network of the SSD as the basic network for target detection. In the basic network, the depthwise-separated deformable convolution module proposed in this paper is used to extract the features of an underwater target so as to improve the target detection accuracy and speed in the complex underwater environment. It mainly fuses the depthwise separable convolution when the deformable convolution acquires the offset of a convolution core, thus reducing the number of parameters and achieving the purposes of increasing the speed of the convolution neural network and enhancing its robustness through sparse representation. The experimental results show that, compared with the SSD detection model that uses the ResNet convolution neural network as the basic network, the improved SSD detection model that uses the depthwise-separated deformable convolution module improves the accuracy of underwater target detection by 11 percentage points and reduces the detection time by 3 ms, thus validating the effectiveness of the algorithm proposed in the paper.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Cong Lin ◽  
Yongbin Zheng ◽  
Xiuchun Xiao ◽  
Jialun Lin
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Disease Diagnosis ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Single Shot ◽  
Detection Model ◽  
Network Output ◽  
Artificial Intelligence Technology ◽  
Chest X Ray

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network. Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.

scholarly journals Underwater Target Detection Based on Deep Neural Network and Image Enhancement

2021 ◽  
Vol 2029 (1) ◽  
pp. 012145
Author(s):  
Yunliang Zheng ◽  
Mengxue Yu ◽  
Zi’ao Ma ◽  
Rong Liu ◽  
Yang Liu
Keyword(s):  
Neural Network ◽  
Image Enhancement ◽  
Target Detection ◽  
Deep Neural Network ◽  
Underwater Target Detection ◽  
Underwater Target

scholarly journals A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Dongmei Shi ◽  
Hongyu Tang
Keyword(s):  
Face Recognition ◽  
Target Detection ◽  
Detection Algorithm ◽  
Bayesian Optimization ◽  
Detection Accuracy ◽  
Small Target ◽  
Sample Distribution ◽  
Feature Pyramid ◽  
Basic Network ◽  
Small Targets

Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.

scholarly journals Ship Target Detection Algorithm Based on Improved Faster R-CNN

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 959 ◽  
Author(s):  
Qi ◽  
Li ◽  
Chen ◽  
Wang ◽  
Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Texture Features ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Marine Transportation ◽  
Computational Speed ◽  
Neural Network Algorithm ◽  
Detection Search

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.

scholarly journals Application of Spatio-Temporal Context and Convolution Neural Network (CNN) in Grooming Behavior of Bactrocera minax (Diptera: Trypetidae) Detection and Statistics

Insects ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 565
Author(s):  
Zhiliang Zhang ◽  
Wei Zhan ◽  
Zhangzhang He ◽  
Yafeng Zou
Keyword(s):  
Neural Network ◽  
Insect Behavior ◽  
Convolution Neural Network ◽  
Temporal Context ◽  
Detection Accuracy ◽  
Grooming Behavior ◽  
Behavior Detection ◽  
Detection Model ◽  
Spatio Temporal ◽  
The Difference

Statistical analysis and research on insect grooming behavior can find more effective methods for pest control. Traditional manual insect grooming behavior statistical methods are time-consuming, labor-intensive, and error-prone. Based on computer vision technology, this paper uses spatio-temporal context to extract video features, uses self-built Convolution Neural Network (CNN) to train the detection model, and proposes a simple and effective Bactrocera minax grooming behavior detection method, which automatically detects the grooming behaviors of the flies and analysis results by a computer program. Applying the method training detection model proposed in this paper, the videos of 22 adult flies with a total of 1320 min of grooming behavior were detected and analyzed, and the total detection accuracy was over 95%, the standard error of the accuracy of the behavior detection of each adult flies was less than 3%, and the difference was less than 15% when compared with the results of manual observation. The experimental results show that the method in this paper greatly reduces the time of manual observation and at the same time ensures the accuracy of insect behavior detection and analysis, which proposes a new informatization analysis method for the behavior statistics of Bactrocera minax and also provides a new idea for related insect behavior identification research.

scholarly journals Lightweight target detection model for embedded platform

2021 ◽  
Vol 2078 (1) ◽  
pp. 012033
Author(s):  
Yuhuan Li ◽  
Jie Wang ◽  
Baodai Shi
Keyword(s):  
Target Detection ◽  
Layer Structure ◽  
Remote Sensing Data ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Embedded Devices ◽  
Data Set ◽  
Detection Model ◽  
Embedded Platform ◽  
Detection Speed

Abstract The detection speed of target detection algorithm depends on the performance of computer equipment. Aiming at the problems of slow detection speed and difficult trade-off between detection accuracy and detection speed when the target detection model is used in embedded devices, a lightweight target detection model based on the improved Tiny YOLO-V3 is proposed. Firstly, we analyze the time consumption of each layer structure in the convolutional neural network, and do a lot of experiments and tests. Then, we compress the time-consuming structure substantially. Secondly, we propose the segmentation and fusion module to improve the detection accuracy. Finally, we use the remote sensing data set of Wuhan University for experiments, and the experimental results show that compared with Tiny YOLO-V3, the detection speed is improved by 4 times, and the accuracy is improved by 2 percentage points.

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