FINED: Fast Inference Network for Edge Detection

• URL: http://arxiv.org/abs/2012.08392v1
• Date: Tue, 15 Dec 2020 16:08:46 GMT
• Title: FINED: Fast Inference Network for Edge Detection
• Authors: Jan Kristanto Wibisono and Hsueh-Ming Hang
• Abstract summary: In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. We propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection.
• Score: 6.809653573125388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we address the design of lightweight deep learning-based edge detection. The deep learning technology offers a significant improvement on the edge detection accuracy. However, typical neural network designs have very high model complexity, which prevents it from practical usage. In contrast, we propose a Fast Inference Network for Edge Detection (FINED), which is a lightweight neural net dedicated to edge detection. By carefully choosing proper components for edge detection purpose, we can achieve the state-of-the-art accuracy in edge detection while significantly reducing its complexity. Another key contribution in increasing the inferencing speed is introducing the training helper concept. The extra subnetworks (training helper) are employed in training but not used in inferencing. It can further reduce the model complexity and yet maintain the same level of accuracy. Our experiments show that our systems outperform all the current edge detectors at about the same model (parameter) size.

Related papers

• Optimizing Sensor Network Design for Multiple Coverage [0.9668407688201359]
  We introduce a new objective function for the greedy (next-best-view) algorithm to design efficient and robust sensor networks. We also introduce a Deep Learning model to accelerate the algorithm for near real-time computations.
  arXiv  Detail & Related papers  (2024-05-15T05:13:20Z)
• Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects [70.48554424894728]
  We develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depth-wise Self-Attention (DSA) module. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-22T06:19:11Z)
• Practical Edge Detection via Robust Collaborative Learning [11.176517889212015]
  Edge detection is a core component in a wide range of visionoriented tasks. To achieve the goal, two key issues should be concerned. How to mitigate deep edge models from inefficient pre-trained backbones. How to liberate the negative influence from noisy or even wrong labels in training data.
  arXiv  Detail & Related papers  (2023-08-27T12:12:27Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• Model2Detector:Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps [12.263417500077383]
  Out-of-distribution detection is an important capability that has long eluded vanilla neural networks. Recent advances in inference-time out-of-distribution detection help mitigate some of these problems. We show how our method consistently outperforms the state-of-the-art in detection accuracy on popular image datasets.
  arXiv  Detail & Related papers  (2022-02-22T23:03:40Z)
• Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network [0.0]
  Edge nodes are crucial for detection against multitudes of cyber attacks on Internet-of-Things endpoints. We develop a novel light, fast and accurate 'Edge-Detect' model, which detects Denial of Service attack on edge nodes using DLM techniques.
  arXiv  Detail & Related papers  (2021-02-03T04:24:34Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Accurate RGB-D Salient Object Detection via Collaborative Learning [101.82654054191443]
  RGB-D saliency detection shows impressive ability on some challenge scenarios. We propose a novel collaborative learning framework where edge, depth and saliency are leveraged in a more efficient way.
  arXiv  Detail & Related papers  (2020-07-23T04:33:36Z)
• Traditional Method Inspired Deep Neural Network for Edge Detection [7.125116757822889]
  We propose a traditional method inspired framework to produce good edges with minimal complexity. Our TIN2 (Traditional Inspired Network) model has an accuracy higher than the recent BDCN2 (Bi-Directional Cascade Network) but with a smaller model.
  arXiv  Detail & Related papers  (2020-05-28T09:20:37Z)
• Depthwise Non-local Module for Fast Salient Object Detection Using a Single Thread [136.2224792151324]
  We propose a new deep learning algorithm for fast salient object detection. The proposed algorithm achieves competitive accuracy and high inference efficiency simultaneously with a single CPU thread.
  arXiv  Detail & Related papers  (2020-01-22T15:23:48Z)
• Towards Unified INT8 Training for Convolutional Neural Network [83.15673050981624]
  We build a unified 8-bit (INT8) training framework for common convolutional neural networks. First, we empirically find the four distinctive characteristics of gradients, which provide us insightful clues for gradient quantization. We propose two universal techniques, including Direction Sensitive Gradient Clipping that reduces the direction deviation of gradients.
  arXiv  Detail & Related papers  (2019-12-29T08:37:53Z)

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.