Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures

• URL: http://arxiv.org/abs/2007.04666v1
• Date: Thu, 9 Jul 2020 09:50:45 GMT
• Title: Building Robust Industrial Applicable Object Detection Models Using Transfer Learning and Single Pass Deep Learning Architectures
• Authors: Steven Puttemans, Timothy Callemein and Toon Goedem\'e
• Abstract summary: We explore how deep convolutional neural networks dedicated to the task of object detection can improve our industrial-oriented object detection pipelines. By using a deep learning architecture that integrates region proposals, classification and probability estimation in a single run, we aim at obtaining real-time performance. We apply these algorithms to two industrially relevant applications, one being the detection of promotion boards in eye tracking data and the other detecting and recognizing packages of warehouse products for augmented advertisements.
• Score: 1.1816942730023883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The uprising trend of deep learning in computer vision and artificial intelligence can simply not be ignored. On the most diverse tasks, from recognition and detection to segmentation, deep learning is able to obtain state-of-the-art results, reaching top notch performance. In this paper we explore how deep convolutional neural networks dedicated to the task of object detection can improve our industrial-oriented object detection pipelines, using state-of-the-art open source deep learning frameworks, like Darknet. By using a deep learning architecture that integrates region proposals, classification and probability estimation in a single run, we aim at obtaining real-time performance. We focus on reducing the needed amount of training data drastically by exploring transfer learning, while still maintaining a high average precision. Furthermore we apply these algorithms to two industrially relevant applications, one being the detection of promotion boards in eye tracking data and the other detecting and recognizing packages of warehouse products for augmented advertisements.

Related papers

• Collaborative Knowledge Infusion for Low-resource Stance Detection [83.88515573352795]
  Target-related knowledge is often needed to assist stance detection models. We propose a collaborative knowledge infusion approach for low-resource stance detection tasks.
  arXiv  Detail & Related papers  (2024-03-28T08:32:14Z)
• Towards Few-Annotation Learning in Computer Vision: Application to Image Classification and Object Detection tasks [3.5353632767823506]
  In this thesis, we develop theoretical, algorithmic and experimental contributions for Machine Learning with limited labels. In a first contribution, we are interested in bridging the gap between theory and practice for popular Meta-Learning algorithms used in Few-Shot Classification. To leverage unlabeled data when training object detectors based on the Transformer architecture, we propose both an unsupervised pretraining and a semi-supervised learning method.
  arXiv  Detail & Related papers  (2023-11-08T18:50:04Z)
• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• Backdoor Attack Detection in Computer Vision by Applying Matrix Factorization on the Weights of Deep Networks [6.44397009982949]
  We introduce a novel method for backdoor detection that extracts features from pre-trained DNN's weights. In comparison to other detection techniques, this has a number of benefits, such as not requiring any training data. Our method outperforms the competing algorithms in terms of efficiency and is more accurate, helping to ensure the safe application of deep learning and AI.
  arXiv  Detail & Related papers  (2022-12-15T20:20:18Z)
• Learning and Retrieval from Prior Data for Skill-based Imitation Learning [47.59794569496233]
  We develop a skill-based imitation learning framework that extracts temporally extended sensorimotor skills from prior data. We identify several key design choices that significantly improve performance on novel tasks.
  arXiv  Detail & Related papers  (2022-10-20T17:34:59Z)
• FasterVideo: Efficient Online Joint Object Detection And Tracking [0.8680676599607126]
  We re-think one of the most successful methods for image object detection, Faster R-CNN, and extend it to the video domain. Our proposed method reaches a very high computational efficiency necessary for relevant applications.
  arXiv  Detail & Related papers  (2022-04-15T09:25:34Z)
• Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation [76.2621758731288]
  We tackle the detection of out-of-distribution (OOD) objects in semantic segmentation. Our main contribution is a new OOD detection architecture called ObsNet associated with a dedicated training scheme based on Local Adversarial Attacks (LAA) We show it obtains top performances both in speed and accuracy when compared to ten recent methods of the literature on three different datasets.
  arXiv  Detail & Related papers  (2021-08-03T17:09:56Z)
• 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)
• 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)

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.