To meet the application requirements of real-time monitoring and object detection on mobile platforms in high-speed dynamic scenarios, the optimized YOLOv8 model is deployed on a smartphone platform for validation. Considering the limited computational resources of mobile devices, the model is optimized using the NCNN (neural computing neural network) framework in combination with model quantization techniques. NCNN is a high-performance open-source neural network inference framework specifically designed for low-power and resource-constrained devices, providing fast and low-latency inference across multiple platforms through optimized memory management and computational pipelines. Ultimately, the optimized YOLOv8 model is successfully deployed on mobile devices, enabling a real-time monitoring application tailored for kart-racing scenarios.

To evaluate the performance of the proposed method in high-speed dynamic scenarios, a kart-racing object detection dataset was constructed using both online image collection and on-site video recordings. Key frames were extracted from recorded videos, and the final dataset consists of 2337 images. The constructed kart-racing dataset will be made available upon reasonable request to the corresponding author for research purposes.

All images were manually annotated using the LabelImg tool, covering nine object categories relevant to kart-racing scenes: whole kart body (KT), front head of the ego kart (KHF), left apex lane marking (ApexL), finish line (FL), right apex lane marking (ApexR), approaching kart facing the camera (KHInv), half kart head on the left side (KHL), half kart head on the right side (KHR), pedestrian (P), and gantry structure (LMJ).

The distribution of samples across categories is moderately imbalanced. The whole kart body (KT) and ego front head (KHF) contain relatively more instances due to their frequent occurrence in racing scenarios, while other categories such as pedestrians and gantry structures appear less frequently but remain sufficiently represented for training and evaluation.

The dataset includes images captured under different lighting conditions, primarily during daytime and late afternoon, thereby introducing moderate illumination variation.

The dataset was divided into training, validation, and test sets with a ratio of 8:1:1.

The experimental environment required is shown in Table 1.

All models were trained using the SGD (stochastic gradient descent) optimizer with an initial learning rate of 0.01, a momentum of 0.937, and a weight decay of 0.01. A cosine annealing learning-rate schedule was adopted during training. Input images were resized to 640×640, and training was conducted for 150 epochs with a batch size of 64. All models were trained from scratch without using pre-trained weights to ensure fair comparison across different configurations.

To comprehensively evaluate the performance of the RIL-YOLO model, multiple evaluation metrics were adopted. These include precision (P), recall (R), F1 score, mAP, Params, FLOPs (floating point operations per second), and FPS. Precision, recall, and F1 score evaluate classification performance, while mAP measures overall detection accuracy by computing the area under the precision–recall curve. Params represent the total number of learnable parameters in the model, reflecting model size and memory requirements. FLOPs indicate the computational complexity required for a single forward pass. FPS evaluates inference efficiency and real-time performance. Their calculation formulas are given as follows: 11P=TPTP+FP,R=TPTP+FN,12F1=2×P×RP+R,mAP=1n∑i=1nAPi,13FPS=1Tinf, where TP, FP, and FN are the true positive, false positive, and false negative, respectively; AP is numerically equal to the area enclosed by the P–R curve and the coordinate axis; and Tinf is the average inference time per image. FPS values were measured on the mobile device by continuously processing 500 frames. The reported FPS corresponds to the mean ± standard deviation across repeated measurements. Other metrics were obtained on the PC platform.

To systematically analyze the contribution of each improvement, all optimization modules were incrementally integrated into the experiments. The experimental results are presented in Table 2, which provides a detailed comparison of the performance gains contributed by each module. Here, A, B, C, and D denote motion blur augmentation, inner-CIoU loss, the RSCD head, and LAMP pruning(ratio=1.5), respectively.

The experimental results indicate that motion blur augmentation enhances robustness in relation to dynamic scenes, achieving a recall of 90.7 % when used alone. When combined with inner-CIoU and RSCD, precision and recall become more balanced, leading to a notable improvement in mAP@0.5. Inner-CIoU alone increases precision to 90.3 % but reduces recall to 85.1 %; however, introducing RSCD restores recall to above 88 % while maintaining high mAP@0.5, highlighting the importance of coordination between localization optimization and detection head design. RSCD further reduces model parameters by 21.6 % with minimal performance degradation, providing a solid basis for lightweight deployment. After applying LAMP pruning, the parameter count is further reduced to 0.51 M, resulting in a 25 % increase in inference speed.

The combination of RSCD and inner-CIoU exhibits a synergistic effect. RSCD focuses on structural optimization by sharing convolutional parameters across feature scales, which improves feature consistency and reduces overfitting under limited model capacity. In contrast, Inner-CIoU addresses optimization dynamics by balancing gradient magnitudes for high- and low-quality samples. When combined, RSCD provides stronger and more compact feature representations, while inner-CIoU ensures stable and balanced regression updates. This complementary interaction leads to more robust convergence and improved overall performance.

To validate the overall performance advantages of the proposed improved algorithm, several representative lightweight models were selected for comparison, including YOLOv8-fastnet, YOLOv8-mobilenetv4, YOLOv5n, YOLOv9t (Wang et al., 2024b), YOLOv10n (Wang et al., 2024a), and YOLOv11n. Among them, YOLOv8-fastnet and YOLOv8-mobilenetv4 replace the original backbone of YOLOv8 with FastNet (Chen et al., 2023) and MobileNetV4 (Qin et al., 2024), respectively. Since the proposed method is built upon the YOLOv8 framework and primarily targets lightweight real-time deployment on mobile devices, the comparative analysis focuses on representative lightweight one-stage detectors. Two-stage detectors (e.g., faster R-CNN) are generally more computationally intensive and less suitable for mobile real-time applications and therefore were not included in this comparison. For fairness, all comparison models, including YOLOv11n, were implemented using the official Ultralytics implementation (version 8.3.50). The comparative experimental results are summarized in Table 3.

The experimental results demonstrate that our method outperforms all comparison models on both mAP@0.5 and mAP@[0.5:0.95], achieving improvements of 1.2 % and 2.1 % over the second-best model, YOLOv9t, respectively, which verifies its strong adaptability to complex scenarios. With respect to model efficiency, the parameter count of our method is only 25.9 % of that of YOLOv9t, while the FLOPs are reduced by 39.7 % compared with YOLOv11n, demonstrating an effective balance between high accuracy and low computational cost.