## 4 Experimental Results
### Results  
The performance of the proposed method was examined in a series of mining scenarios, including detection trucks, vehicles, and pedestrians, using the TG-Mine-3D dataset for both quantitative and qualitative analyses. To compare the detection results with those of other methods, we separated objects into L-shaped and point models according to the number of points and clustering categories, inspired by [39]. Quantitative Analysis. We evaluated the performance regarding 3D average precision (AP) and mean average precision (mAP), which are consistent with the KITTI evaluation detection metrics. For a fair comparison, the same intersection over union (IoU) thresholds were used for the same category of all methods: 0.7 for 'truck' and'vehicle', and 0.5 for 'pedestrian'. This is unlike KITTI, which divides objects into three difficulty levels (easy, moderate, and hard) depending on the size, occlusion level, and truncation of 3D objects. We considered that all objects had a similar level of difficulty owing to the low probability of occlusion for each object in a mining environment. The proposed method was compared to our method without the ROI segment, the method in [11; 40], SECOND [29], PointPillars [21], PointRCNN [22], and TANET [23], whose performance was highlighted in 3D object detection. The results of AP, mAP, and inference time are shown in Table 2.  
The proposed method, 3 DSG, achieved a 3D mAP of 66.39%, outperforming traditional state-of-the-art methods [11; 40], and deep learning methods SECOND [29], PointPillars [21], and PointRCNN [22] by about 43.11%, 31.63%, 4.17%, 1.49%, and 1.97%, respectively. Although TANet [23] outperformed our method by 0.02% for 3D mAP, our method showed more robustness for the categories of 'truck' and 'pedestrian'. Compared with traditional methods [11; 40], the proposed method outperformed regarding detection in all categories. As the unstructured road was uneven, the detection performance of the plane fitting method [11] decreased. Due to the complex geometric structure of unstructured road boundaries, the traditional detection methods [11; 40] falsely detected road boundaries as objects such as trucks, resulting in poor detection performance. Owing to the strategy of road region extraction, they filtered out most nonground point clouds off the road region, such as the road boundaries, which improved the robustness of the object detection results. Compared with deep learning methods SECOND [29], PointPillars [21], PointRCNN [22], and TANET [23], the proposed method achieved the best performance in the truck and pedestrian categories, but showed poor detection performance in vehi \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Truck** & **Vehicle** & **Pedestrian** & **3D mAP (\%)** & **Running Time (ms)** \\ \hline Method in [11] & 31.85 & 20.38 & 17.62 & 23.28 & 16.25 \\ Method in [40] & 52.35 & 25.16 & 26.76 & 34.76 & 25.32 \\ \hline SECOND [29] & 75.86 & 71.18 & 39.61 & 62.21 & 49.52 \\ PointPillars [21] & 80.35 & 72.52 & 41.83 & 64.90 & **15.63** \\ PointRCNN [22] & 79.62 & **73.53** & 40.11 & 64.42 & 98.76 \\ TANet [23] & 81.29 & 71.44 & 46.51 & **66.41** & 31.26 \\ \hline
3 DSG without ROI & 56.15 & 31.25 & 28.31 & 38.57 & 110.69 \\
3 DSG & **83.95** & 62.91 & **52.31** & 66.39 & 51.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration parameters for the LiDAR. cles. For large objects (e.g., trucks), the proposed method achieved improvements of 8.09%, 3.60%, 4.33%, and 2.66% over SECOND, PointPillars, PointRCNN, and TANET, respectively. This was due to the proposed method fusing semantic segmentation information with geometric features to classify object categories, which achieved high classification accuracy for large objects such as trucks. For challenging objects (e.g., pedestrians), the proposed method achieved improvements of 12.7%, 10.48%, 12.20%, and 5.80% over SECOND, PointPillars, PointRCNN, and TANET, respectively.