## 4 Experimental Results
### Methods Evaluation  
Due to the time-intensive nature of validation precision assessments, which require processing all images in the validation set, validations are performed every 10 epochs. To evaluate the model against comparison methods, two additional models are trained at input resolutions of 352 \(\times\) 352 and 320 \(\times\) 320, respectively. This approach allows for assessing the proposed model's performance across different resolutions and identifying the optimal balance of performance for various application scenarios. The server configuration used for training the models is outlined in Table 3. Training parameter settings are summarized in Table 4.  
After training, the model is converted to ONNX format and deployed on three different embedded development boards using NCNN for inference testing. The performance comparison with state-of-the-art methods is illustrated in Table 5 and Figure 8. The inference times reported in Table 5 are the result of deploying the models on the RK3566 development board using NCNN and averaging the outcomes of 100 inferences with the NCNN benchmark tool. In the experimental evaluation on the RK3566-embedded plat \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Setting** \\ \hline Optimizer & Adam \\ Learning rate & 0.001 \\ Learning rate decay strategy & Cosine \\ Batch size & 64 \\ CUDA version & 12.01 \\ Training framework & PyTorch 1.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Configuration of training parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Parameters** \\ \hline CPU model & Intel Core i7-10700 \\ Memory capacity & 32 GB \\ GPU model & Nvidia RTX3090 \\ GPU memory capacity & 24 GB \\ Operating system & Ubuntu 22.04 \\ CUDA version & 12.01 \\ Training framework & PyTorch 1.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental hardware and software. form, the proposed object-detection model significantly excels in balancing precision and inference speed, which is crucial for real-time applications in resource-constrained environments. Compared to state-of-the-art models such as YOLOv8s, which achieves a higher mAP of 17.3% and AP50 of 31.00% at the expense of longer inference times (140.72 ms), the model demonstrates superior efficiency. Specifically, at a trained resolution of 320 \(\times\) 320, the proposed model attains a mAP of 10.8% and AP50 of 21% with an impressive inference speed of only 12.13 ms. Increasing the input resolution to 352 \(\times\) 352 not only maintains rapid processing with an inference time of 15.27 ms but also enhances mAP to 11.5% and AP50 to 23.05%. These results highlight the proposed model's ability to effectively leverage computational resources, making it particularly suitable for embedded systems where both speed of detection and low power consumption are paramount. Furthermore, the model's performance with a reduced parameter count of only 0.4035 M significantly enhances its applicability for real-time object-detection tasks, establishing it as an optimal solution for advanced embedded applications that demand high efficiency and accuracy. The investigation utilizes the Visdrome dataset to conduct comprehensive testing of object-detection algorithms across various embedded platforms, examining different core configurations including single-core, dual-core, and quad-core setups. The initial analysis focuses on a quad-core configuration, where the proposed model demonstrates superior performance. Specifically, on the RK3566 platform, the model achieves an impressive 65.49 FPS, and on the H618, it reaches 32.88 FPS, markedly surpassing the FastestDet model, which achieves 48.85 FPS and 26.57 FPS, respectively, under identical conditions. This translates to an enhancement in inference speed of 34.06% for the  
Figure 8: Comparison of mAP and inference time with object-detection models on RK3566.