Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network

Wu, Jian-jun; Liao, Shaowen

doi:10.1155/2022/4285436

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…Repeat the operation stage by stage, thus transmitting the deep strong semantic information to the shallow layer. And PANet is the bottomup propagation path [23]. Firstly, the large-sized feature map is downsampled 2-fold.…”

Section: Yolov5m Algorithmmentioning

confidence: 99%

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

Du,

Jiang

2024

CMC

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To address the challenges of high complexity, poor real-time performance, and low detection rates for small target vehicles in existing vehicle object detection algorithms, this paper proposes a real-time lightweight architecture based on You Only Look Once (YOLO) v5m. Firstly, a lightweight upsampling operator called Content-Aware Reassembly of Features (CARAFE) is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles, reducing the missed detection rate and false detection rate. Secondly, a new prediction layer for tiny targets is added, and the feature fusion network is redesigned to enhance the detection capability for small targets. Finally, this paper applies L1 regularization to train the improved network, followed by pruning and fine-tuning operations to remove redundant channels, reducing computational and parameter complexity and enhancing the detection efficiency of the network. Training is conducted on the VisDrone2019-DET dataset. The experimental results show that the proposed algorithm reduces parameters and computation by 63.8% and 65.8%, respectively. The average detection accuracy improves by 5.15%, and the detection speed reaches 47 images per second, satisfying real-time requirements. Compared with existing approaches, including YOLOv5m and classical vehicle detection algorithms, our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.

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Section: Yolov5m Algorithmmentioning

confidence: 99%

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

Du,

Jiang

2024

CMC

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“…Wang et al [ 27 ] used the RFP structure to replace the original SPP structure and added attention mechanisms CBAM and CA structures to the backbone and neck layers of the model, which ultimately reduced the parameters of the model and improved the inference speed. Wu and Liao [ 28 ] improved the SSD, used RFM to improve the receptive field and semantics of the predicted feature map, and introduced a path aggregation network to fuse multi-scale features to improve the location and classification accuracy of traffic signs. Yang and Bingfeng [ 29 ] proposed a lightweight real-time traffic sign detection integration framework based on YOLO by combining deep learning methods.…”

Section: Related Workmentioning

confidence: 99%

Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Wang

Bai

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Traffic sign detection is a challenging problem in the field of unmanned driving, particularly important in complex environments. We propose a method, based on the improved You only look once (YOLO) v4, to detect and recognize multiscale traffic signs in complex environments. This method employs an image preprocessing module that can classify and denoize images of complex environments and then input the images into the improved YOLOv4. We also design an improved feature pyramid structure to replace the original feature pyramid of YOLOv4. This structure uses an adaptive feature fusion module and a multiscale feature transfer mechanism to reduce putative information loss in the feature map generation process and improve the information transfer between deep and shallow features, enhancing the representation ability of feature pyramids. Finally, we use EIOU LOSS and Cluster-NMS to further improve the model performance. The experimental results on the fusion of Tsinghua-Tencent 100 K and our collected dataset show that the proposed method achieves an mAP of 81.78%. Compared to existing methods, our method demonstrates its superiority with regard to traffic sign detection.

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“…With the increasing scale of Internet users, the Internet has begun to carry an increasing number of emerging network applications, and accurate trafc classifcation is the premise of the basic tasks of the network. Especially with the wide application of encryption data transmission, network trafc encryption is becoming a standard [1][2][3][4]. Encryption will make abnormal behaviors in the network such as botnet [5], worm [6], image transmission [7,8], and denial of service attack [9] more covert.…”

Section: Introductionmentioning

confidence: 99%

Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction

Long

Zhang

2023

Computational Intelligence and Neuroscience

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With an increasing number of network attacks using encrypted communication, the anomaly detection of encryption traffic is of great importance to ensure reliable network operation. However, the existing feature extraction methods for encrypted traffic anomaly detection have difficulties in extracting features, resulting in their low efficiency. In this paper, we propose a framework of encrypted traffic anomaly detection based on parallel automatic feature extraction, called deep encrypted traffic detection (DETD). The proposed DETD uses a parallel small-scale multilayer stack autoencoder to extract local traffic features from encrypted traffic and then adopts an L1 regularization-based feature selection algorithm to select the most representative feature set for the final encrypted traffic anomaly detection task. The experimental results show that DETD has promising robustness in feature extraction, i.e., the feature extraction efficiency of DETD is 66% higher than that of the conventional stacked autoencoder, and the anomaly detection performance is as high as 99.998%, and thus DETD outperforms the deep full-range framework and other neural network anomaly detection algorithms.

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Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network

Cited by 12 publications

References 22 publications

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

A Real-Time Small Target Vehicle Detection Algorithm with an Improved YOLOv5m Network Model

Multiscale Traffic Sign Detection Method in Complex Environment Based on YOLOv4

Deep Encrypted Traffic Detection: An Anomaly Detection Framework for Encryption Traffic Based on Parallel Automatic Feature Extraction

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