CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions

• URL: http://arxiv.org/abs/2309.06902v4
• Date: Sat, 3 Feb 2024 09:06:59 GMT
• Title: CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
• Authors: Haoqin Hong, Yue Zhou, Xiangyu Shu and Xiaofang Hu
• Abstract summary: CCSPNet is an efficient feature extraction module based on Contextual Transformer and CNN. We propose a joint training model, CCSPNet-Joint, to improve data efficiency and generalization. Experiments have shown that CCSPNet achieves state-of-the-art performance in traffic sign detection under extreme conditions.
• Score: 3.6190463374643795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic sign detection is an important research direction in intelligent driving. Unfortunately, existing methods often overlook extreme conditions such as fog, rain, and motion blur. Moreover, the end-to-end training strategy for image denoising and object detection models fails to utilize inter-model information effectively. To address these issues, we propose CCSPNet, an efficient feature extraction module based on Contextual Transformer and CNN, capable of effectively utilizing the static and dynamic features of images, achieving faster inference speed and providing stronger feature enhancement capabilities. Furthermore, we establish the correlation between object detection and image denoising tasks and propose a joint training model, CCSPNet-Joint, to improve data efficiency and generalization. Finally, to validate our approach, we create the CCTSDB-AUG dataset for traffic sign detection in extreme scenarios. Extensive experiments have shown that CCSPNet achieves state-of-the-art performance in traffic sign detection under extreme conditions. Compared to end-to-end methods, CCSPNet-Joint achieves a 5.32% improvement in precision and an 18.09% improvement in mAP@.5.

Related papers

• YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion [15.571409945909243]
  We present a novel real-time and efficient road sign detection network, YOLO-TS. This network significantly improves performance by optimizing the receptive fields of multi-scale feature maps. Our innovative feature-fusion strategy, leveraging the flexibility of Anchor-Free methods, achieves remarkable enhancements in both accuracy and speed.
  arXiv  Detail & Related papers  (2024-10-22T16:19:55Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• Semantic Communication Enabling Robust Edge Intelligence for Time-Critical IoT Applications [87.05763097471487]
  This paper aims to design robust Edge Intelligence using semantic communication for time-critical IoT applications. We analyze the effect of image DCT coefficients on inference accuracy and propose the channel-agnostic effectiveness encoding for offloading.
  arXiv  Detail & Related papers  (2022-11-24T20:13:17Z)
• Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach [76.45949280328838]
  We propose a Laplacian enhanced low-rank tensor (LETC) framework featuring both lowrankness and multi-temporal correlations for large-scale traffic speed kriging. We then design an efficient solution algorithm via several effective numeric techniques to scale up the proposed model to network-wide kriging.
  arXiv  Detail & Related papers  (2022-10-21T07:25:57Z)
• Improved YOLOv5 network for real-time multi-scale traffic sign detection [4.5598087061051755]
  We propose an improved feature pyramid model, named AF-FPN, which utilize the adaptive attention module (AAM) and feature enhancement module (FEM) to reduce the information loss in the process of feature map generation. We replace the original feature pyramid network in YOLOv5 with AF-FPN, which improves the detection performance for multi-scale targets of the YOLOv5 network.
  arXiv  Detail & Related papers  (2021-12-16T11:02:12Z)
• A Deep Value-network Based Approach for Multi-Driver Order Dispatching [55.36656442934531]
  We propose a deep reinforcement learning based solution for order dispatching. We conduct large scale online A/B tests on DiDi's ride-dispatching platform. Results show that CVNet consistently outperforms other recently proposed dispatching methods.
  arXiv  Detail & Related papers  (2021-06-08T16:27:04Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• BiconNet: An Edge-preserved Connectivity-based Approach for Salient Object Detection [3.3517146652431378]
  We show that our model can use any existing saliency-based SOD framework as its backbone. Through comprehensive experiments on five benchmark datasets, we demonstrate that our proposed method outperforms state-of-the-art SOD approaches.
  arXiv  Detail & Related papers  (2021-02-27T21:39:04Z)
• CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images [0.9462808515258465]
  In this paper, we discuss the role of discriminative features in object detection. We then propose a Critical Feature Capturing Network (CFC-Net) to improve detection accuracy. We show that our method achieves superior detection performance compared with many state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-01-18T02:31:09Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)
• DFR-TSD: A Deep Learning Based Framework for Robust Traffic Sign Detection Under Challenging Weather Conditions [4.0075294089613465]
  We propose a Convolutional Neural Network (CNN) based traffic sign recognition framework with prior enhancement. We experimentally show that our method obtains an overall precision and recall of 91.1% and 70.71% that is 7.58% and 35.90% improvement in precision and recall, respectively.
  arXiv  Detail & Related papers  (2020-06-03T23:12:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.