Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness

• URL: http://arxiv.org/abs/2405.17004v1
• Date: Mon, 27 May 2024 09:47:49 GMT
• Title: Efficient Visual Fault Detection for Freight Train via Neural Architecture Search with Data Volume Robustness
• Authors: Yang Zhang, Mingying Li, Huilin Pan, Moyun Liu, Yang Zhou,
• Abstract summary: We propose an efficient NAS-based framework for visual fault detection of freight trains. First, we design a scale-aware search space for discovering an effective receptive field in the head. Second, we explore the robustness of data volume to reduce search costs based on the specifically designed search space.
• Score: 6.5769476554745925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning-based fault detection methods have achieved significant success. In visual fault detection of freight trains, there exists a large characteristic difference between inter-class components (scale variance) but intra-class on the contrary, which entails scale-awareness for detectors. Moreover, the design of task-specific networks heavily relies on human expertise. As a consequence, neural architecture search (NAS) that automates the model design process gains considerable attention because of its promising performance. However, NAS is computationally intensive due to the large search space and huge data volume. In this work, we propose an efficient NAS-based framework for visual fault detection of freight trains to search for the task-specific detection head with capacities of multi-scale representation. First, we design a scale-aware search space for discovering an effective receptive field in the head. Second, we explore the robustness of data volume to reduce search costs based on the specifically designed search space, and a novel sharing strategy is proposed to reduce memory and further improve search efficiency. Extensive experimental results demonstrate the effectiveness of our method with data volume robustness, which achieves 46.8 and 47.9 mAP on the Bottom View and Side View datasets, respectively. Our framework outperforms the state-of-the-art approaches and linearly decreases the search costs with reduced data volumes.

Related papers

• DONNAv2 -- Lightweight Neural Architecture Search for Vision tasks [6.628409795264665]
  We present the next-generation neural architecture design for computationally efficient neural architecture distillation - DONNAv2. DONNAv2 reduces the computational cost of DONNA by 10x for the larger datasets. To improve the quality of NAS search space, DONNAv2 leverages a block knowledge distillation filter to remove blocks with high inference costs.
  arXiv  Detail & Related papers  (2023-09-26T04:48:50Z)
• $\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search [85.84110365657455]
  We propose a simple-but-efficient regularization method, termed as Beta-Decay, to regularize the DARTS-based NAS searching process. Experimental results on NAS-Bench-201 show that our proposed method can help to stabilize the searching process and makes the searched network more transferable across different datasets.
  arXiv  Detail & Related papers  (2022-03-03T11:47:14Z)
• NAS-FCOS: Efficient Search for Object Detection Architectures [113.47766862146389]
  We propose an efficient method to obtain better object detectors by searching for the feature pyramid network (FPN) and the prediction head of a simple anchor-free object detector. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs.
  arXiv  Detail & Related papers  (2021-10-24T12:20:04Z)
• Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics [117.4281417428145]
  This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS) NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. We present a unified framework to understand and accelerate NAS, by disentangling "TEG" characteristics of searched networks.
  arXiv  Detail & Related papers  (2021-08-26T17:52:07Z)
• Poisoning the Search Space in Neural Architecture Search [0.0]
  We evaluate the robustness of one such algorithm known as Efficient NAS against data poisoning attacks on the original search space. Our results provide insights into the challenges to surmount in using NAS for more adversarially robust architecture search.
  arXiv  Detail & Related papers  (2021-06-28T05:45:57Z)
• Searching Efficient Model-guided Deep Network for Image Denoising [61.65776576769698]
  We present a novel approach by connecting model-guided design with NAS (MoD-NAS) MoD-NAS employs a highly reusable width search strategy and a densely connected search block to automatically select the operations of each layer. Experimental results on several popular datasets show that our MoD-NAS has achieved even better PSNR performance than current state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-04-06T14:03:01Z)
• Contrastive Self-supervised Neural Architecture Search [6.162410142452926]
  This paper proposes a novel cell-based neural architecture search algorithm (NAS) Our algorithm capitalizes on the effectiveness of self-supervised learning for image representations. An extensive number of experiments empirically show that our search algorithm can achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2021-02-21T08:38:28Z)
• MS-RANAS: Multi-Scale Resource-Aware Neural Architecture Search [94.80212602202518]
  We propose Multi-Scale Resource-Aware Neural Architecture Search (MS-RANAS) We employ a one-shot architecture search approach in order to obtain a reduced search cost. We achieve state-of-the-art results in terms of accuracy-speed trade-off.
  arXiv  Detail & Related papers  (2020-09-29T11:56:01Z)
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning [72.99415402575886]
  Outlier detection is an important data mining task with numerous practical applications. We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
  arXiv  Detail & Related papers  (2020-06-19T18:57:51Z)
• Selective Convolutional Network: An Efficient Object Detector with Ignoring Background [28.591619763438054]
  We introduce an efficient object detector called Selective Convolutional Network (SCN), which selectively calculates only on the locations that contain meaningful and conducive information. To solve it, we design an elaborate structure with negligible overheads to guide the network where to look next.
  arXiv  Detail & Related papers  (2020-02-04T10:07:01Z)

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.