Research on automatic target detection and recognition based on deep learning

Wang, Jia; Liu, Chen; Fu, Tian; Zheng, Lili

doi:10.1016/j.jvcir.2019.01.017

Cited by 16 publications

(5 citation statements)

References 28 publications

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“…With the emergence of various labeled datasets and powerful computing resources such as GPU, deep neural network has been widely used in the field of image target detection and recognition [8]. Using the deep neural network model, the specific characteristics of the target object can be obtained through the training of different levels of modules.…”

Section: Related Workmentioning

confidence: 99%

Image Target Detection and Recognition Method Using Deep Learning

Sun

2022

Advances in Multimedia

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Image target detection and recognition had been widely used in many fields. However, the existing methods had poor robustness; they not only had high error rate of target recognition but also had high dependence on parameters, so they were limited in application. Therefore, this paper proposed an image target detection and recognition method based on the improved R-CNN model, so as to detect and recognize the dynamic image target in real time. Based on the analysis of the existing theories of deep learning detection and recognition, this paper summarized the composition and working principle of the traditional image target detection and recognition system and compared the basic models of target detection and recognition, such as R-CNN network, Fast-RCNN network, and Faster-RCNN network. In order to improve the accuracy and real-time performance of the model in image target detection and recognition, this paper adopted the target feature matching module in the existing R-CNN network model, so as to obtain the feature map close to the same target through similarity calculation for the features extracted by the model. Therefore, an image target detection and recognition algorithm based on the improved R-CNN network model is proposed. Finally, the experimental results showed that the image target detection and recognition algorithm proposed in this paper can be better applied to image target detection and classification in complex environment and had higher detection efficiency and recognition accuracy than the existing models. The target detection and recognition algorithm proposed in this paper had certain reference value and guiding significance for further application research in related fields.

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Section: Related Workmentioning

confidence: 99%

Image Target Detection and Recognition Method Using Deep Learning

Sun

2022

Advances in Multimedia

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“…YOLOv3 adopts the K-means clustering analysis algorithm; K-means clustering algorithm obtains anchor prior box, uses Random.choice methods to obtain the random cluster centre. According to the cluster family, it is divided into (C1, C2,....Ck), the minimum square error of the target is calculated as formula (5), and then the final cluster centre is obtained by adjusting the error slowly. Therefore, its calculation will be affected by different data or uneven distribution of arrays.…”

Section: Optimisation Of Clustering Algorithmmentioning

confidence: 99%

“…One of the important research contents is target detection, which aims to find the targets in the image and determines the position of the target objects. It is the basis of computer vision and widely used in many applications, such as face detection, intelligent transportation, industrial defect detection and so on [4,5]. In the development of intelligent transportation systems, target detection is an essential part.…”

Section: Introductionmentioning

confidence: 99%

An improved YOLOv3‐tiny algorithm for vehicle detection in natural scenes

Huang

Lin

et al. 2021

IET Cyber-Syst and Robotics

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YOLO (You Only Look Once), as a target detection algorithm with good speed and precision, is widely used in the industry. In the process of driving, the vehicle image captured by the driving camera is detected and it extracts the license plate and the front part of the vehicle. Compared with the network structure of YOLOv3-tiny algorithm, the acquisition method of anchor box is improved by combining the Birch algorithm. In order to improve the real-time performance, the original two-scale detection is added to the multi-scale prediction of three-scale detection to ensure its accuracy. Finally, the experimental results show that the improved YOLOv3-tiny network structure proposed in this study can improve the performance of mean-average-precision, intersection over union and speed by 5.99%, 17.52% and 48.4%, respectively, and the algorithm has certain robustness.How to cite this article: Huang, B., et al.: An improved YOLOv3-tiny algorithm for vehicle detection in natural scenes. IET Cyber-Syst. Robot. 1-9 (2021).

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“…Recently, deep learning-based target detection has experienced rapid development across various fields [22][23][24][25]. Notable strides have been achieved in aircraft target recognition in Synthetic Aperture Radar (SAR) images through deep learning [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

FEMSFNet: Feature Enhancement and Multi-Scales Fusion Network for SAR Aircraft Detection

Zhu,

Zhang,

et al. 2024

Remote Sensing

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Aircraft targets, as high-value subjects, are a focal point in Synthetic Aperture Radar (SAR) image interpretation. To tackle challenges like limited SAR aircraft datasets and shortcomings in existing detection algorithms (complexity, poor performance, weak generalization), we present the Feature Enhancement and Multi-Scales Fusion Network (FEMSFNet) for SAR aircraft detection. FEMSFNet employs diverse image augmentation and integrates optimized Squeeze-and-Excitation Networks (SE) with residual network (ResNet) in a SdE-Resblock structure for a lightweight yet accurate model. It introduces ssppf-CSP module, an improved pyramid pooling model, to prevent receptive field deviation in deep network training. Tailored for SAR aircraft detection, FEMSFNet optimizes loss functions, emphasizing both speed and accuracy. Evaluation on the SAR Aircraft Detection Dataset (SADD) demonstrates significant improvements compared to the contrasted algorithms: precision rate (92%), recall rate (96%), and F1 score (94%), with a maximum increase of 12.2% in precision, 12.9% in recall, and 13.3% in F1 score.

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Research on automatic target detection and recognition based on deep learning

Cited by 16 publications

References 28 publications

Image Target Detection and Recognition Method Using Deep Learning

Image Target Detection and Recognition Method Using Deep Learning

An improved YOLOv3‐tiny algorithm for vehicle detection in natural scenes

FEMSFNet: Feature Enhancement and Multi-Scales Fusion Network for SAR Aircraft Detection

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