基于关注特征聚合的自动驾驶目标检测改进轻量级网络

IF 1.6 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Journal of Low Power Electronics and Applications Pub Date : 2023-08-10 DOI:10.3390/jlpea13030049
Priyank Kalgaonkar, M. El-Sharkawy
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引用次数: 0

摘要

物体检测是一种比图像分类更高级的计算机视觉应用,它利用深度神经网络预测输入图像中的物体,并通过边界框确定它们的位置。人工智能领域越来越关注自动驾驶的需求,这需要高精度和快速的推理速度。本研究论文旨在通过引入专门为自动驾驶车辆设计的高效轻量级目标检测网络来解决这一需求。该网络被命名为MobDet3,将改进的MobileNetV3作为其主干,利用其轻量级卷积神经网络算法提取和聚合图像特征。此外,该网络集成了计算机视觉中的新技术,并适应PyTorch框架的最新迭代。MobDet3网络不仅增强了目标定位能力,而且提高了特征图在不同尺度上的可重用性。利用自动驾驶数据集以及大规模的日常人类和物体数据集,进行了广泛的评估,以评估所提出网络的有效性。这些评估是在NXP BlueBox 2.0上进行的,这是一个为自动驾驶汽车设计的先进边缘开发平台。结果表明,所提出的轻量级目标检测网络在BDD100K数据集上的平均精度高达58.30%,在NXP BlueBox 2.0上的推理速度高达每秒88.92帧,非常适合自动驾驶应用中的实时目标检测。
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An Improved Lightweight Network Using Attentive Feature Aggregation for Object Detection in Autonomous Driving
Object detection, a more advanced application of computer vision than image classification, utilizes deep neural networks to predict objects in an input image and determine their locations through bounding boxes. The field of artificial intelligence has increasingly focused on the demands of autonomous driving, which require both high accuracy and fast inference speeds. This research paper aims to address this demand by introducing an efficient lightweight network for object detection specifically designed for self-driving vehicles. The proposed network, named MobDet3, incorporates a modified MobileNetV3 as its backbone, leveraging its lightweight convolutional neural network algorithm to extract and aggregate image features. Furthermore, the network integrates altered techniques in computer vision and adjusts to the most recent iteration of the PyTorch framework. The MobDet3 network enhances not only object positioning ability but also the reusability of feature maps across different scales. Extensive evaluations were conducted to assess the effectiveness of the proposed network, utilizing an autonomous driving dataset, as well as large-scale everyday human and object datasets. These evaluations were performed on NXP BlueBox 2.0, an advanced edge development platform designed for autonomous vehicles. The results demonstrate that the proposed lightweight object detection network achieves a mean precision of up to 58.30% on the BDD100K dataset and a high inference speed of up to 88.92 frames per second on NXP BlueBox 2.0, making it well-suited for real-time object detection in autonomous driving applications.
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来源期刊
Journal of Low Power Electronics and Applications
Journal of Low Power Electronics and Applications Engineering-Electrical and Electronic Engineering
CiteScore
3.60
自引率
14.30%
发文量
57
审稿时长
11 weeks
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