In the context of crowd counting, most of the works have focused on improving the accuracy without regard to the performance leading to algorithms that are not suitable for embedded applications. In this paper, we propose a lightweight convolutional neural network architecture to perform crowd detection and counting using fewer computer resources without a significant loss on count accuracy. The architecture was trained using the Bayes loss function to further improve its accuracy and then pruned to further reduce the computational resources used. The proposed architecture was tested over the USF-QNRF achieving a competitive Mean Average Error of 154.07 and a superior Mean Square Error of 241.77 while maintaining a competitive number of parameters of 0.067 Million. The obtained results suggest that the Bayes loss can be used with other architectures to further improve them and also the last convolutional layer provides no significant information and even encourage over-fitting at training.

中文翻译：

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使用轻量级架构进行密集人群检测和计数

在人群计数的背景下，大多数工作都集中在提高准确性而不考虑导致算法不适合嵌入式应用程序的性能。在本文中，我们提出了一种轻量级卷积神经网络架构，以使用更少的计算机资源执行人群检测和计数，而不会显着降低计数精度。该架构使用贝叶斯损失函数进行训练以进一步提高其准确性，然后进行修剪以进一步减少使用的计算资源。所提出的架构在 USF-QNRF 上进行了测试，实现了 154.07 的有竞争力的平均误差和 241.77 的优越均方误差，同时保持了 06.7 万个有竞争力的参数数量。