## 4 Experiment and Results Analysis
### Experiment on the Improved YOLOv8 Object Detection Algorithm  
To evaluate the effectiveness of the YOLOv8 algorithm enhanced by the MSF convolution module, this experiment utilizes the VOC2007 dataset [23], a standard benchmark for object detection, accessible at [[http://host.robots.ox.ac.uk/pascal/VOC/voc2007/](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/)]([http://host.robots.ox.ac.uk/pascal/VOC/voc2007/](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/)) (accessed on 21 June 2024). The dataset contains 20 categories, including person, car, sheep, and others. It is split into training, validation, and test sets in a 6:2:2 ratio. The training set consists of 5977 images, the validation set includes 1993 images, and the test set also comprises 1993 images. Object detection algorithms are commonly evaluated using the mean Average Precision (mAP) metric. A higher mAP value indicates better model performance. The mAP is calculated as follows:  
\[mAP=\frac{1}{C}\sum_{\text{j=1}}^{\text{c}}\text{AP}_{\text{j}} \tag{16}\]  
In Equation (16), \(\text{AP}_{\text{j}}\) represents the average precision for a specific class, where c denotes the total number of sample categories, and j refers to the current class sample. The detection results obtained using the improved YOLOv8 are presented in Figure 4. As shown in Figure 4a,b, even small and distant objects, which are barely visible, are accurately recognized. Building on the preliminary results presented in Table 3, this section provides a detailed analysis of the performance of the proposed YOLOv8n+MSFConv model. The evaluation focuses on standard metrics, including precision (P), recall (R), mean Average Precision at IoU = 0.5 (mAP@50), and mAP across IoU thresholds from 0.5 to 0.95 (mAP@50:95), to quantify detection accuracy. Additionally, we examine the computational complexity through parameters (Params), GFLOPs, and inference speed (Frames Per Second, FPS) to assess the trade-offs associated with the improved detection performance. All experiments were conducted on an NVIDIA GeForce RTX 4060 Laptop GPU, ensuring a consistent hardware environment for fair comparison. Figure 4: Detection Results. (**a**) Only a small portion of the features are visible. (**b**) Small targets at a long distance. Table 3 highlights that YOLOv8n+MSFConv achieves a precision of 74.2%, marking a 6.31% improvement over the baseline YOLOv8n's 69.8%. The mAP@50 increases from 64.0% to 65.1% (a 1.72% improvement), and the mAP@50:95 rises from 42.9% to 43.5% (a 1.40% improvement). These gains indicate that the multi-scale feature extraction capability of MSFConv effectively captures diverse object features, leading to more accurate detection in dynamic environments. However, this enhanced performance comes with a modest increase in computational complexity. The parameter count of YOLOv8n+MSFConv is 3.19 million, a 6.0% increase compared to YOLOv8n's 3.01 million, and the GFLOPs rise from 8.1 to 8.9, reflecting a 9.9% increase in computational demand. Consequently, the inference speed decreases, with the FPS dropping from 275.1 for YOLOv8n to 233.1 for YOLOv8n+MSFConv, a 15.3% reduction. Despite this trade-off, the FPS of 233.1 remains well above the threshold of 30 FPS typically required for real-time SLAM applications, ensuring practical applicability in dynamic scenarios. The superior performance of YOLOv8n+MSFConv can be attributed to its multi-scale feature consolidation mechanism, which leverages parallel convolutional branches (3 \(\times\) 3, 5 \(\times\) 5, and 7 \(\times\) 7) to extract features at varying scales. This approach enhances the model's ability to detect objects of different sizes and shapes, a critical requirement in dynamic indoor scenes where objects like people or furniture may vary significantly in scale. The slight decrease in recall (from 58.9% to 57.8%) suggests that while MSFConv improves precision by reducing false positives, it may occasionally miss some true positives, possibly due to the increased model complexity affecting feature generalization.