YOLOv4-tiny is a lightweight network designed for low-end devices. It proposes a global model scaling technology, which uses the same scaling method with few convolution filters in each stage of the network, resulting in a small receptive field and low accuracy. To solve this problem, we use different scaling techniques in shallow and deep network stages. For the shallow network stage, the Simple-StemBlock scaling module is proposed to simplify network based on factors such as FLOPs and network fragmentation. The module can effectively reduce computation and improve the diversity of features. For the deep network stage, we consider depth of network and hardware resource constraints, the Depth-CSPBlock scaling module is designed to expand receptive field while keeping the computation as low as possible and layered residual connection is built in the module to enrich semantic information. Besides, mish activation function is adopted in the backbone for higher accuracy. The experimental results show that the accuracy of the proposed method achieves 23.2% AP on MSCOCO test-dev and 66.73  %  AP₅₀ on VOC datasets, compared with YOLOv4-tiny, the AP and AP₅₀ increased by 3.3% and 3.7%, respectively. The speed of the proposed method on Jetson TX2 can reach 45.6 frames per second, which is 20% faster than YOLOv4-tiny.