pedestrian detection for advanced driving assisting system: a transfer learning approach
pedestrian detection for advanced driving assisting system: a transfer learning approach
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高级驾驶辅助系统的行人检测：一种迁移学习方法
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Abdessalem Ben Abdelaali
中科院分区：
文献类型：
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作者：
R. Ayachi;Mouna Afif;Yahia Said;Abdessalem Ben Abdelaali
行人检测是一项必须集成到先进驾驶辅助系统中的重要任务。对于行人检测任务，必须遵守许多规则，如高性能、实时处理和轻量化，以适应ADAS的嵌入式设备。本文提出了一种基于卷积神经网络的行人检测系统。CNN是一种深度学习模型，由于其在图像处理和决策方面的能力，通常用于分类和检测等计算机视觉任务。拟议中的CNN模型被命名为Yolov3 Tiny。它最早用于一般目标的检测。在这项工作中，我们将转移学习技术应用于所提出的CNN模型，使其适用于行人检测。使用美国加州理工学院的行人检测数据集对所提出的模型进行训练和评估。该模型的平均精度为76.7%，推理时间为202FPS。
pedestrian detection is an important task that must be integrated into an advanced driving assisting system (ADAS). For a pedestrian detection task many rules must be respected like high performance, real-time processing, and lightweight size to fit into the embedded device of the ADAS. In this paper, we propose a pedestrian detection system based on a convolutional neural network (CNN). CNN is a deep learning model generally used for computer vision tasks like classification and detection because of its power in image processing and decision making. The proposed CNN model is named Yolov3 tiny. It was firstly used for general object detection. In this work, we applied the transfer learning technique on the proposed CNN model to make it suitable for pedestrian detection. The pedestrian detection dataset Caltech US was used to train and evaluate the proposed model. The model achieves an average precision of 76.7% and an inference time of 202 FPS.