An Analysis of Deep Object Detectors For Diver Detection

• URL: http://arxiv.org/abs/2012.05701v1
• Date: Wed, 25 Nov 2020 01:50:32 GMT
• Title: An Analysis of Deep Object Detectors For Diver Detection
• Authors: Karin de Langis, Michael Fulton, Junaed Sattar
• Abstract summary: We produce a dataset of approximately 105,000 annotated images of divers sourced from videos. We train a variety of state-of-the-art deep neural networks for object detection, including SSD with Mobilenet, Faster R-CNN, and YOLO. Based on our results, we recommend Tiny-YOLOv4 for real-time applications on robots.
• Score: 19.14344722263869
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the end goal of selecting and using diver detection models to support human-robot collaboration capabilities such as diver following, we thoroughly analyze a large set of deep neural networks for diver detection. We begin by producing a dataset of approximately 105,000 annotated images of divers sourced from videos -- one of the largest and most varied diver detection datasets ever created. Using this dataset, we train a variety of state-of-the-art deep neural networks for object detection, including SSD with Mobilenet, Faster R-CNN, and YOLO. Along with these single-frame detectors, we also train networks designed for detection of objects in a video stream, using temporal information as well as single-frame image information. We evaluate these networks on typical accuracy and efficiency metrics, as well as on the temporal stability of their detections. Finally, we analyze the failures of these detectors, pointing out the most common scenarios of failure. Based on our results, we recommend SSDs or Tiny-YOLOv4 for real-time applications on robots and recommend further investigation of video object detection methods.

Related papers

• Run-time Introspection of 2D Object Detection in Automated Driving Systems Using Learning Representations [13.529124221397822]
  We introduce a novel introspection solution for 2D object detection based on Deep Neural Networks (DNNs) We implement several state-of-the-art (SOTA) introspection mechanisms for error detection in 2D object detection, using one-stage and two-stage object detectors evaluated on KITTI and BDD datasets. Our performance evaluation shows that the proposed introspection solution outperforms SOTA methods, achieving an absolute reduction in the missed error ratio of 9% to 17% in the BDD dataset.
  arXiv  Detail & Related papers  (2024-03-02T10:56:14Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• Semi-supervised 3D Object Detection via Temporal Graph Neural Networks [17.90796183565084]
  3D object detection plays an important role in autonomous driving and other robotics applications. We propose leveraging large amounts of unlabeled point cloud videos by semi-supervised learning of 3D object detectors. Our method achieves state-of-the-art detection performance on the challenging nuScenes and H3D benchmarks.
  arXiv  Detail & Related papers  (2022-02-01T02:06:54Z)
• Finding Facial Forgery Artifacts with Parts-Based Detectors [73.08584805913813]
  We design a series of forgery detection systems that each focus on one individual part of the face. We use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets.
  arXiv  Detail & Related papers  (2021-09-21T16:18:45Z)
• Parallel Detection for Efficient Video Analytics at the Edge [5.547133811014004]
  Deep Neural Network (DNN) trained object detectors are widely deployed in mission-critical systems for real time video analytics at the edge. A common performance requirement in mission-critical edge services is the near real-time latency of online object detection on edge devices. This paper addresses these problems by exploiting multi-model multi-device detection parallelism for fast object detection in edge systems.
  arXiv  Detail & Related papers  (2021-07-27T02:50:46Z)
• DS-Net: Dynamic Spatiotemporal Network for Video Salient Object Detection [78.04869214450963]
  We propose a novel dynamic temporal-temporal network (DSNet) for more effective fusion of temporal and spatial information. We show that the proposed method achieves superior performance than state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2020-12-09T06:42:30Z)
• Video Anomaly Detection Using Pre-Trained Deep Convolutional Neural Nets and Context Mining [2.0646127669654835]
  We show how to use pre-trained convolutional neural net models to perform feature extraction and context mining. We derive contextual properties from the high-level features to further improve the performance of our video anomaly detection method.
  arXiv  Detail & Related papers  (2020-10-06T00:26:14Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)
• 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)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.