Deep-learning-based pedestrian detectors can enhance the capabilities of smart camera systems in a wide spectrum of machine vision applications including video surveillance, autonomous driving, robots and drones, smart factory, and health monitoring. However, such complex paradigms do not scale easily and are not traditionally implemented in resource-constrained smart cameras for on-device processing which offers significant advantages in situations when real-time monitoring and privacy are vital. This work addresses the challenge of achieving a good trade-off between accuracy and speed for efficient deep-learning-based pedestrian detection in smart camera applications. The contributions of this work are the following: 1) a computationally efficient architecture based on separable convolutions that integrates dense connections across layers and multi-scale feature fusion to improve representational capacity while decreasing the number of parameters and operations, 2) a more elaborate loss function for improved localization, 3) and an anchor-less approach for detection. The proposed approach referred to as YOLOpeds is evaluated using the PETS2009 surveillance dataset on 320 × 320 images. A real-system implementation is presented using the Jetson TX2 embedded platform. YOLOpeds provides real-time sustained operation of over 30 frames per second with detection rates in the range of 86% outperforming existing deep learning models.

中文翻译：

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YOLOpeds：针对智能相机应用的高效实时单次行人检测

基于深度学习的行人检测器可以在各种机器视觉应用（包括视频监视，自动驾驶，机器人和无人机，智能工厂和健康监控）中增强智能相机系统的功能。但是，这种复杂的范例无法轻松扩展，并且传统上无法在资源受限的智能相机中进行设备上处理，这在实时监控和隐私至关重要的情况下具有明显优势。这项工作解决了在精度和速度之间取得良好折衷的挑战，以实现智能相机应用中基于深度学习的行人检测的高效。这项工作的贡献如下：1）一种基于可计算卷积的高效计算架构，该架构整合了跨层的密集连接和多尺度特征融合，以提高表示能力，同时减少参数和操作的数量； 2）更为精细的损失函数，用于改善定位； 3）和无锚检测方法。使用PETS2009监视数据集对320×320图像进行了评估，该方法被称为YOLOpeds。使用Jetson TX2嵌入式平台展示了一个真实的系统实现。YOLOpeds提供每秒30帧以上的实时持续操作，检测率在86％的范围内胜过现有的深度学习模型。2）更精细的损失函数以改善定位； 3）无锚检测方法。使用PETS2009监视数据集对320×320图像进行了评估，该方法被称为YOLOpeds。使用Jetson TX2嵌入式平台展示了一个真实的系统实现。YOLOpeds提供每秒30帧以上的实时持续操作，检测率在86％的范围内胜过现有的深度学习模型。2）更精细的损失函数以改善定位； 3）无锚检测方法。使用PETS2009监视数据集对320×320图像进行了评估，该方法被称为YOLOpeds。使用Jetson TX2嵌入式平台展示了一个真实的系统实现。YOLOpeds提供每秒30帧以上的实时持续操作，检测率在86％的范围内胜过现有的深度学习模型。