Comprehensive Analysis of the Object Detection Pipeline on UAVs

• URL: http://arxiv.org/abs/2203.00306v1
• Date: Tue, 1 Mar 2022 09:30:01 GMT
• Title: Comprehensive Analysis of the Object Detection Pipeline on UAVs
• Authors: Leon Amadeus Varga, Sebastian Koch, Andreas Zell
• Abstract summary: We first empirically analyze the influence of seven parameters (quantization, compression, resolution, color model, image distortion, gamma correction, additional channels) in remote sensing applications. We show that not all parameters have an equal impact on detection accuracy and data throughput, and that by using a suitable compromise between parameters we are able to improve detection accuracy for lightweight object detection models.
• Score: 16.071349046409885
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An object detection pipeline comprises a camera that captures the scene and an object detector that processes these images. The quality of the images directly affects the performance of the object detector. Many works nowadays focus either on improving the image quality or improving the object detection models independently, but neglect the importance of joint optimization of the two subsystems. In this paper, we first empirically analyze the influence of seven parameters (quantization, compression, resolution, color model, image distortion, gamma correction, additional channels) in remote sensing applications. For our experiments, we utilize three UAV data sets from different domains and a mixture of large and small state-of-the-art object detector models to provide an extensive evaluation of the influence of the pipeline parameters. Additionally, we realize an object detection pipeline prototype on an embedded platform for an UAV and give a best practice recommendation for building object detection pipelines based on our findings. We show that not all parameters have an equal impact on detection accuracy and data throughput, and that by using a suitable compromise between parameters we are able to improve detection accuracy for lightweight object detection models, while keeping the same data throughput.

Related papers

• Performance Assessment of Feature Detection Methods for 2-D FS Sonar Imagery [11.23455335391121]
  Key challenges include non-uniform lighting and poor visibility in turbid environments. High-frequency forward-look sonar cameras address these issues. We evaluate a number of feature detectors using real sonar images from five different sonar devices.
  arXiv  Detail & Related papers  (2024-09-11T04:35:07Z)
• Towards Consistent Object Detection via LiDAR-Camera Synergy [17.665362927472973]
  There is no existing model capable of detecting an object's position in both point clouds and images. This paper introduces an end-to-end Consistency Object Detection (COD) algorithm framework. To assess the accuracy of the object correlation between point clouds and images, this paper proposes a new evaluation metric, Consistency Precision.
  arXiv  Detail & Related papers  (2024-05-02T13:04:26Z)
• 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)
• Let's Roll: Synthetic Dataset Analysis for Pedestrian Detection Across Different Shutter Types [7.0441427250832644]
  This paper studies the impact of different shutter mechanisms on machine learning (ML) object detection models on a synthetic dataset. In particular, we train and evaluate mainstream detection models with our synthetically-generated paired GS and RS datasets.
  arXiv  Detail & Related papers  (2023-09-15T04:07:42Z)
• Knowledge Distillation for Oriented Object Detection on Aerial Images [1.827510863075184]
  We present a model compression method for rotated object detection on aerial images by knowledge distillation, namely KD-RNet. The experimental result on a large-scale aerial object detection dataset (DOTA) demonstrates that the proposed KD-RNet model can achieve improved mean-average precision (mAP) with reduced number of parameters, at the same time, KD-RNet boost the performance on providing high quality detections with higher overlap with groundtruth annotations.
  arXiv  Detail & Related papers  (2022-06-20T14:24:16Z)
• On the Role of Sensor Fusion for Object Detection in Future Vehicular Networks [25.838878314196375]
  We evaluate how using a combination of different sensors affects the detection of the environment in which the vehicles move and operate. The final objective is to identify the optimal setup that would minimize the amount of data to be distributed over the channel.
  arXiv  Detail & Related papers  (2021-04-23T18:58:37Z)
• Robust and Accurate Object Detection via Adversarial Learning [111.36192453882195]
  This work augments the fine-tuning stage for object detectors by exploring adversarial examples. Our approach boosts the performance of state-of-the-art EfficientDets by +1.1 mAP on the object detection benchmark.
  arXiv  Detail & Related papers  (2021-03-23T19:45:26Z)
• Ensembling object detectors for image and video data analysis [98.26061123111647]
  We propose a method for ensembling the outputs of multiple object detectors for improving detection performance and precision of bounding boxes on image data. We extend it to video data by proposing a two-stage tracking-based scheme for detection refinement.
  arXiv  Detail & Related papers  (2021-02-09T12:38:16Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)
• Perceiving Traffic from Aerial Images [86.994032967469]
  We propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone 2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.
  arXiv  Detail & Related papers  (2020-09-16T11:37:43Z)
• Condensing Two-stage Detection with Automatic Object Key Part Discovery [87.1034745775229]
  Two-stage object detectors generally require excessively large models for their detection heads to achieve high accuracy. We propose that the model parameters of two-stage detection heads can be condensed and reduced by concentrating on object key parts. Our proposed technique consistently maintains original performance while waiving around 50% of the model parameters of common two-stage detection heads.
  arXiv  Detail & Related papers  (2020-06-10T01:20:47Z)

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.