A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey

• URL: http://arxiv.org/abs/2208.10895v1
• Date: Tue, 23 Aug 2022 12:01:16 GMT
• Title: A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey
• Authors: Elahe Arani, Shruthi Gowda, Ratnajit Mukherjee, Omar Magdy, Senthilkumar Kathiresan, Bahram Zonooz
• Abstract summary: Deep neural network based object detectors are continuously evolving and are used in a multitude of applications. While safety-critical applications need high accuracy and reliability, low-latency tasks need resource and energy-efficient networks. A reference benchmark for existing networks does not exist, nor does a standard evaluation guideline for designing new networks.
• Score: 9.861721674777877
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural network based object detectors are continuously evolving and are used in a multitude of applications, each having its own set of requirements. While safety-critical applications need high accuracy and reliability, low-latency tasks need resource and energy-efficient networks. Real-time detectors, which are a necessity in high-impact real-world applications, are continuously proposed, but they overemphasize the improvements in accuracy and speed while other capabilities such as versatility, robustness, resource and energy efficiency are omitted. A reference benchmark for existing networks does not exist, nor does a standard evaluation guideline for designing new networks, which results in ambiguous and inconsistent comparisons. We, thus, conduct a comprehensive study on multiple real-time detectors (anchor-, keypoint-, and transformer-based) on a wide range of datasets and report results on an extensive set of metrics. We also study the impact of variables such as image size, anchor dimensions, confidence thresholds, and architecture layers on the overall performance. We analyze the robustness of detection networks against distribution shifts, natural corruptions, and adversarial attacks. Also, we provide a calibration analysis to gauge the reliability of the predictions. Finally, to highlight the real-world impact, we conduct two unique case studies, on autonomous driving and healthcare applications. To further gauge the capability of networks in critical real-time applications, we report the performance after deploying the detection networks on edge devices. Our extensive empirical study can act as a guideline for the industrial community to make an informed choice on the existing networks. We also hope to inspire the research community towards a new direction in the design and evaluation of networks that focuses on a bigger and holistic overview for a far-reaching impact.

Related papers

• TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection [0.5261718469769447]
  Existing datasets often fall short, lacking the necessary diversity and alignment with the contemporary network environment. This paper introduces TII-SSRC-23, a novel and comprehensive dataset designed to overcome these challenges.
  arXiv  Detail & Related papers  (2023-09-14T05:23:36Z)
• Leveraging a Probabilistic PCA Model to Understand the Multivariate Statistical Network Monitoring Framework for Network Security Anomaly Detection [64.1680666036655]
  We revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view. We have evaluated the mathematical model using two different datasets.
  arXiv  Detail & Related papers  (2023-02-02T13:41:18Z)
• Influencer Detection with Dynamic Graph Neural Networks [56.1837101824783]
  We investigate different dynamic Graph Neural Networks (GNNs) configurations for influencer detection. We show that using deep multi-head attention in GNN and encoding temporal attributes significantly improves performance.
  arXiv  Detail & Related papers  (2022-11-15T13:00:25Z)
• An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks [0.0]
  We develop an intelligent, efficient, and updatable intrusion detection system by applying an important machine learning concept known as ensemble learning. In this paper, we examine the application of different homogeneous and heterogeneous online ensembles in sensory data analysis. Among the proposed novel online ensembles, both the heterogeneous ensemble consisting of an Adaptive Random Forest (ARF) combined with the Hoeffding Adaptive Tree (HAT) algorithm and the homogeneous ensemble HAT made up of 10 models achieved higher detection rates of 96.84% and 97.2%, respectively.
  arXiv  Detail & Related papers  (2022-04-28T23:10:47Z)
• 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)
• Topological Uncertainty: Monitoring trained neural networks through persistence of activation graphs [0.9786690381850356]
  In industrial applications, data coming from an open-world setting might widely differ from the benchmark datasets on which a network was trained. We develop a method to monitor trained neural networks based on the topological properties of their activation graphs.
  arXiv  Detail & Related papers  (2021-05-07T14:16:03Z)
• Robust Object Detection via Instance-Level Temporal Cycle Confusion [89.1027433760578]
  We study the effectiveness of auxiliary self-supervised tasks to improve the out-of-distribution generalization of object detectors. Inspired by the principle of maximum entropy, we introduce a novel self-supervised task, instance-level temporal cycle confusion (CycConf) For each object, the task is to find the most different object proposals in the adjacent frame in a video and then cycle back to itself for self-supervision.
  arXiv  Detail & Related papers  (2021-04-16T21:35:08Z)
• Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition [77.95361323613147]
  Current state-of-the-art visual recognition systems rely on pretraining a neural network on a large-scale dataset and finetuning the network weights on a smaller dataset. In this work, we prove that dynamically adapting network architectures tailored for each domain task along with weight finetuning benefits in both efficiency and effectiveness. Our method can be easily generalized to an unsupervised paradigm by replacing supernet training with self-supervised learning in the source domain tasks and performing linear evaluation in the downstream tasks.
  arXiv  Detail & Related papers  (2021-03-31T08:15:17Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Characterization and Identification of Cloudified Mobile Network Performance Bottlenecks [0.0]
  This study is a first attempt to experimentally explore the range of performance bottlenecks that 5G mobile networks can experience. In particular, we find that distributed analytics performs reasonably well both in terms of bottleneck identification accuracy and incurred computational and communication overhead.
  arXiv  Detail & Related papers  (2020-07-22T14:46:51Z)
• Feature Extraction for Novelty Detection in Network Traffic [18.687465197576415]
  Data representation plays a critical role in the performance of novelty detection methods in machine learning. We release an open-source tool, an accompanying Python library, and an end-to-end pipeline for novelty detection in network traffic.
  arXiv  Detail & Related papers  (2020-06-30T17:53:59Z)

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.