EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection

• URL: http://arxiv.org/abs/2408.14189v1
• Date: Mon, 26 Aug 2024 11:26:27 GMT
• Title: EMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection
• Authors: Pengyu Li, Chenhe Liu, Tengfei Li, Xinyu Wang, Shihui Zhang, Dongyang Yu,
• Abstract summary: The detection of small objects, particularly traffic signs, is a critical subtask within object detection and autonomous driving. Motivated by these challenges, we propose a novel object detection network named Efficient Multi-scale and Diverse Feature Network (EMDFNet) EMDFNet integrates an Augmented Shortcut Module and an Efficient Hybrid to address the aforementioned issues simultaneously.
• Score: 11.525603303355268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The detection of small objects, particularly traffic signs, is a critical subtask within object detection and autonomous driving. Despite the notable achievements in previous research, two primary challenges persist. Firstly, the main issue is the singleness of feature extraction. Secondly, the detection process fails to effectively integrate with objects of varying sizes or scales. These issues are also prevalent in generic object detection. Motivated by these challenges, in this paper, we propose a novel object detection network named Efficient Multi-scale and Diverse Feature Network (EMDFNet) for traffic sign detection that integrates an Augmented Shortcut Module and an Efficient Hybrid Encoder to address the aforementioned issues simultaneously. Specifically, the Augmented Shortcut Module utilizes multiple branches to integrate various spatial semantic information and channel semantic information, thereby enhancing feature diversity. The Efficient Hybrid Encoder utilizes global feature fusion and local feature interaction based on various features to generate distinctive classification features by integrating feature information in an adaptable manner. Extensive experiments on the Tsinghua-Tencent 100K (TT100K) benchmark and the German Traffic Sign Detection Benchmark (GTSDB) demonstrate that our EMDFNet outperforms other state-of-the-art detectors in performance while retaining the real-time processing capabilities of single-stage models. This substantiates the effectiveness of EMDFNet in detecting small traffic signs.

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)
• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection [21.104686670216445]
  We propose DR-MOFS to model the feature selection problem in network intrusion detection as a three-objective optimization problem. In most cases, the proposed method can outperform previous methods, i.e., lead to fewer features, higher accuracy and detection rate.
  arXiv  Detail & Related papers  (2024-06-13T14:42:17Z)
• Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving [69.20604395205248]
  We present a new 3D point-based detector model, named Shift-SSD, for precise 3D object detection in autonomous driving. We introduce an intriguing Cross-Cluster Shifting operation to unleash the representation capacity of the point-based detector. We conduct extensive experiments on the KITTI, runtime, and nuScenes datasets, and the results demonstrate the state-of-the-art performance of Shift-SSD.
  arXiv  Detail & Related papers  (2024-03-10T10:36:32Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• 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)
• Specificity-preserving RGB-D Saliency Detection [103.3722116992476]
  We propose a specificity-preserving network (SP-Net) for RGB-D saliency detection. Two modality-specific networks and a shared learning network are adopted to generate individual and shared saliency maps. Experiments on six benchmark datasets demonstrate that our SP-Net outperforms other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-18T14:14:22Z)
• Multi-object Tracking with a Hierarchical Single-branch Network [31.680667324595557]
  We propose an online multi-object tracking framework based on a hierarchical single-branch network. Our novel iHOIM loss function unifies the objectives of the two sub-tasks and encourages better detection performance. Experimental results on MOT16 and MOT20 datasets show that we can achieve state-of-the-art tracking performance.
  arXiv  Detail & Related papers  (2021-01-06T12:14:58Z)
• AFD-Net: Adaptive Fully-Dual Network for Few-Shot Object Detection [8.39479809973967]
  Few-shot object detection (FSOD) aims at learning a detector that can fast adapt to previously unseen objects with scarce examples. Existing methods solve this problem by performing subtasks of classification and localization utilizing a shared component. We present that a general few-shot detector should consider the explicit decomposition of two subtasks, as well as leveraging information from both of them to enhance feature representations.
  arXiv  Detail & Related papers  (2020-11-30T10:21:32Z)
• 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)
• FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking [92.48078680697311]
  Multi-object tracking (MOT) is an important problem in computer vision. We present a simple yet effective approach termed as FairMOT based on the anchor-free object detection architecture CenterNet. The approach achieves high accuracy for both detection and tracking.
  arXiv  Detail & Related papers  (2020-04-04T08:18:00Z)

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.