# Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection
## 4 Experimental Results
In the figures, the white dotted line indicates the ground truth, and the red and blue solid lines indicate the bounding box of the object detected by MYs-WM and Y-CM, respectively. In particular, in Figure 9, for the white bounding box representing the ground truth of pedestrians, Y-CM hardly detected pedestrians due to the shadow effect, while MYs-WM detected almost all of them. The performance evaluation method for the KITTI dataset is generally divided into three difficulty levels of 'easy','moderate', and 'hard', depending on the size of the object to be detected and the degree of truncation. 'Easy' means 'fully visible' with at least 40 pixels,'moderate' is 'partial occlusion' with at least 25 pixels, 'hard' is 'higher occlusion', and the number of pixels is equal to'moderate' [27]. Therefore, an additional performance evaluation was carried out based on AP and missed-detection rate according to the difficulty level of pedestrians and cars, and the detection results are presented in Tables 3 and 4. Additional comparison and evaluation with [28], a system of detecting objects by input \begin{table}
\begin{tabular}{c|c c c} \hline  & \multicolumn{3}{c}{**AP [\%]**} \\ \hline
**Detection Architecture** & **IOU = 0.3** & **IOU = 0.5** & **IOU = 0.7** \\ \hline Y-CM & 91.25 & 90.80 & 87.12 \\ Y-DM & 82.99 & 81.84 & 70.57 \\ Y-RM & 83.88 & 82.19 & 68.59 \\ MYs-WM (ours) & 93.64 & 93.53 & 89.83 \\ MLF\_HHA\_Faster-RCNN [1] & - & - & 87.90 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of detection of cars according to intersection of union (IOU). Figure 8: Comparison of car detection examples with Y-CM and MYs-WM. of the left and right images from a stereo camera, its difference map, and the extracted features from PCD into the deformable part models were also performed. For the detection of cars and pedestrians, the proposed MYs-WM showed the highest performance at all difficulty levels; in particular, MYs-WM showed much higher AP compared to [28] at difficulty levels of 'easy' and'moderate'. In addition, Figure 10 demonstrates the effectively improved missed-detection rate of the proposed system. Improvement in detection performance is not as high with the new method at the level of 'hard.' This is because single-step object detection algorithms, such as YOLO or SSD, are relatively fast in real time compared to other object detection algorithms due to spatial constraints but are limited in detecting small objects rated as 'hard.' The CNN-based object detection algorithm extracts low-level features from the input image through the convolution layer and then classifies the objects through fully connected nodes. The smaller is an object, the more information about features extracted through the convolutional layer is lost. In particular, YOLO detects images divided into lattice regions using a single regression method, so the detection performance for small objects or multiple overlapping objects is lower than that of the two-stage object detection algorithm of the R-CNN series. With the exception of the 'hard' level, MYs-WM showed relatively high detection performance compared to other detection systems. This is because the detection rate is improved by reinforcing the detection result even though the objects to be detected differ according to the characteristics of the sensor used in the proposed fusion system. Since RGB cameras express data through high-resolution  
Figure 9: Comparison of pedestrian detection examples with Y-CM and MYs-WM. pixels with a wide range of values from 0 to 255, the shape and color of objects are clearly expressed, and detection performance is high. However, detection performance is degraded when affected by external environmental factors such as lighting, shadows, and obstacles. On the other hand, a LiDAR uses a high-pulse laser derived from the sensor itself to acquire the reflected data and is robust to external environmental factors; however, its low resolution results in low object detection performance under normal circumstances.