基于改进YOLOv4-tiny的轻量化室内人员目标检测算法

深度学习在室内人员检测领域应用广泛，但是传统的卷积神经网络复杂度大且需要高算力GPU的支持，很难实现在嵌入式设备上的部署。针对上述问题，该文提出一种基于改进YOLOv4-tiny的轻量化室内人员目标检测算法。首先，设计一种改进的Ghost卷积特征提取模块，有效减少了模型的复杂度；同时，该文通过采用带有通道混洗机制的深度可分离卷积进一步减少网络参数；其次，该文构建了一种多尺度空洞卷积模块以获得更多具有判别性的特征信息，并结合改进的空洞空间金字塔池化结构和具有位置信息的注意力机制进行有效的特征融合，在提升准确率的同时提高推理速度。在多个数据集和多种硬件平台上的实验表明，该文算法在精度、速度、模型参数和体积等方面优于原YOLOv4-tiny网络，更适合部署于资源有限的嵌入式设备。

Lightweight Indoor Personnel Detection Algorithm Based on Improved YOLOv4-tiny

Deep learning has been widely applied to the field of indoor personnel detection. However, the traditional convolutional neural networks have a high complexity and require the support of highly computational GPU. It is difficult to accomplish the implementation in the embedded devices. For the above problems, a lightweight network model based on improved YOLOv4-tiny network is proposed for indoor personnel detection. Firstly, an improved Ghost convolution feature extraction module is designed to reduce effectively the model complexity. Simultaneously, to reduce network parameters, a depth-wise separable convolution with channel shuffle mechanism is adopted in this paper. Secondly, a multi-scale dilated convolution module is developed in this paper to obtain more discriminative feature information, which combines the improved dilated space pyramid pooling module and the attention mechanism with location information for effective feature fusion, thereby improving inference accuracy and inference speed, simultaneously. The experiments on multiple datasets and hardware platforms show that the proposed model is superior to the original YOLOv4-tiny network in terms of accuracy, speed, model parameters and volume. Therefore, the proposed model is more suitable for deployment in resource-limited embedded devices.