# YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network
## 4 Results
### Results and Discussion  
We introduced a heatmap [28; 29] to observe the model for high-altitude safety belt feature extraction and selected samples for the qualitative analysis to verify the effectiveness of the YOLO-DFAN algorithm. The baseline network and YOLO-DFAN were employed to predict and generate heatmaps for three samples presented in Figures 4-6. Figure 5 reveals the detection results of the samples that were correctly predicted by both networks. According to Figure 5a,b, both models correctly predicted the high-altitude safety belts. Comparing the heatmaps in Figure 5c,d, the feature extraction of the baseline network was relatively rough. The focus of the high-altitude safety belt is more on the lower part of the human body and less on the central area of the safety belt. The improved network was more precise in feature extraction. According to Figure 5d, the region of interest covers the entire high-altitude safety belt region, and the region of interest of the high-altitude safety belt is more accurate. Figure 6 presents the detection results for a worker not wearing a high-altitude safety belt. According to Figure 6a,b, observing the results of the baseline network and YOLO-DFAN, the baseline network made a false detection, whereas YOLO-DFAN correctly predicted that no high-altitude safety belt was worn. Comparing the heatmaps of the two sample outputs in Figure 6c,d, due to the overlap between the worker on the left and the  
Figure 5: Results of correctly predicted samples: (**a**) prediction of YOLOv4-tiny; (**b**) prediction of YOLO-DFAN; (**c**) heatmap of YOLOv4-tiny; (**d**) heatmap of YOLO-DFAN. Figure 6: Results of original network misdetection samples: (**a**) prediction of YOLOv4-tiny; (**b**) prediction of YOLO-DFAN; (**c**) heatmap of YOLOv4-tiny; (**d**) heatmap of YOLO-DFAN. power facility, the baseline network mistakenly focused on the pocket area on the thigh of the worker on the left, resulting in false detection. Figure 7 shows the detection situation with a smaller object and a more complicated detection scene. The leftmost of the three workers is not wearing a high-altitude safety belt, while the middle and right workers are wearing safety belts. According to Figure 7a,b, observing the detection of the original network and YOLO-DFAN, the baseline network missed the high-altitude safety belt status of the rightmost worker, whereas YOLO-DFAN correctly detected all of their safety belt statuses. Comparing the generated heatmaps in Figure 7c,d, the baseline network extracted the features of the middle high-altitude safety belt wearer very broadly and roughly, while it failed to extract the features of the high-altitude safety belt status of the rightmost worker. The feature extraction of the middle high-altitude safety belt wearer was very general and inexact, and the network did not extract the features of the region of interest of the high-altitude safety belt worn by the rightmost worker. In contrast, YOLO-DFAN refined the feature extraction of the high-altitude safety belt worn by the middle worker, while accurately noting the region of interest of the high-altitude safety belt worn by the rightmost worker. To prove the performance advantage of our algorithm, we compared and analyzed different evaluation indices for different algorithms on the test set, as shown in Table 1. From Table 1, we can draw the following conclusions: On the test set, compared with the YOLOv4-tiny, YOLO-DFAN improved R from 67.33% to 71.29%, improved mAP from 74.03% to 79.16%, improved F1 from 0.76 to 0.80, and improved P from 84.44 to 84.56, mainly benefiting from the ASPP module refining the object features extracted by the backbone, while the DFAN attention network proposed in this paper enhanced the model's ability to extract the regions of interest, and the introduction of PANet enhanced the network's ability to detect multiscale objects. Compared with the EfficientDet, YOLO-DFAN also showed a better performance.