# FANet: Improving 3D Object Detection with Position Adaptation
## 4 Experimental Results and Analysis
### Data and Analysis  
To verify the effectiveness of the algorithm, the PV-RCNN before and after optimization will run in the same environment. Compare mean average precision with other advanced methods. According to Figures 4 and 5, FANet has faster network convergence than PV-RCNN. FANet only needs a limited number of iterations to achieve good results, while PV-RCNN needs more iterations to achieve the same effect as FANet. In this paper, to show that our method has higher accuracy than PV-RCNN, we selected five random sets of images for comparison. The (a) of each set of images is the camera photo, the (b) of each set of images is the result of the point cloud information identified using PV-RCNN, and the (c) of each set of images is the result of the point cloud information identified using FANet. We used the image information to determine which method was better and more accurate for object detection. As shown in Figure 6, according to the image information, FANet can recognize the truck on the front right, while PV-RCNN cannot accurately recognize it. As shown in Figure 7, according to the image information, FANet can recognize the car in front, while PV-RCNN cannot accurately recognize it. As shown in Figure 8, according to the image information, FANet can recognize the distant car on the front right, while PV-RCNN cannot accurately recognize it. As shown in Figure 9, according to the image information, FANet can recognize the distant car in front, while PV-RCNN cannot accurately recognize it. As shown in Figure 10, according to the image information, FANet can recognize the distant car in front, while PV-RCNN cannot accurately recognize it. In the above five sets of scenes, the image information provided strong evidence that FANet outperforms PV-RCNN in terms of object detection accuracy. Specifically, FANet is able to identify objects in a variety of scenes, demonstrating its superior ability to detect objects in complex and distant scenes. In contrast, PV-RCNN was unable to accurately identify objects in these distant scenes. When we consider both the camera photos and the point cloud data, it becomes even more apparent that FANet is a more accurate 3D object detector than PV-RCNN. The point cloud data allow our method to extract key features from the scene, and our approach enables us to effectively deal with point cloud irregularity and disorder. These features, combined with the ability to accurately recognize objects in complex scenes, lead to significantly improved accuracy compared to PV-RCNN. In this paper, we recorded the average accuracy of each type of object recognition with an IoU threshold of 0.7 (car) and 0.5 (pedestrian and cyclist). Tables 1-3 show the comparison between the object detection effect of the PV-RCNN network model before and after optimization for car, pedestrian, and cyclist. Table 4 shows how our method compares with state-of-the-art methods with respect to mAP. Figure 4: Loss of PC-RCNN. Figure 5: Loss of FANet. Tables 2-4 display the experimental results of the FANet model on the KITTI validation dataset. The accuracy improvements achieved using FANet are noteworthy, with almost all of the projects surpassing the state-of-the-art PV-RCNN method. In particular, the average accuracy of the car class improved by 3.05%, demonstrating the effectiveness of the proposed new network in improving the target detection accuracy. The pedestrian class showed the highest increase, with a maximum improvement of 1.76% in terms of average precision. Similarly, the average precision of the cyclist class increased by up to 3.07%. Although PV-RCNN has a slight advantage in pedestrian and cyclist BEV detection, the proposed FANet model demonstrates superior overall performance in terms of accuracy improvement. These results validate the effectiveness of the proposed PAConv module in enhancing the performance of 3D object detection models.