<p>Accurate and real-time recognition of right-of-way information transmitted by traffic lights is key to ensuring the safety of traffic participants. Existing deep learning-based traffic light detection and recognition (TLDR) models achieved high accuracy. However, these models require considerable computing power, which makes them difficult to be deployed on mobile platforms such as intelligent vehicles, urban low-speed unmanned work platforms, and visually impaired street-crossing assistance devices. In this paper, a mobile platform-friendly TLDR model HCEDSM (high computational cost-effectiveness and detection speed model) is proposed to achieve high accuracy, low latency, and strong deployment feasibility. First, a lightweight backbone network combined with EfficientViT and efficient multi-scale attention is introduced to reduce the quantity of computation and focus on small target features. Second, a cross-scale neck network is constructed to improve the feature fusion ability with lower computational cost. The open-source S2TLD dataset is used for training and testing, and the model is deployed on the NVIDIA Jetson Nano B01, which is a representative platform for low-computing-power devices. The results show that HCEDSM achieves a precision of 94.7% with 2.4 GFLOPs and a detection speed of up to 12.2 FPS on the NVIDIA Jetson Nano B01. The detection results on LISA traffic Light dataset and BSTLD (Bosch Small Traffic Light Dataset) show that the model has good generalization ability. These findings demonstrate that HCEDSM enables accurate and real-time recognition of traffic lights on resource-constrained platforms.</p>