# Automatic Handgun Detection with Deep Learning in Video Surveillance Images
## 4 Results
of pose information using the method previously described slightly worsened the handgun detection for the Faster R-CNN and RetinaNet models, obtaining lower AP values in exp. 2, 4, and 6 than those obtained in the counterparts experiments without the pose. For these two models, the addition of the pose information not only reduced the average precision (AP), but also the recall value because of the increase of the undetected handguns (FNs). This effect may be due to the fact that these models employ architectures that \"learn\" from the original dataset (without the pose) implicit complex characteristics associated with the pose, so that the blending of the skeletons obtained with OpenPose had an effect analogous to the addition of \"noise\", which hinders detection. A significantly different effect could be observed in the experiments carried out with the YOLOv3 models (exp. 7 and 8). In these experiments, the detection results offered an improvement of 1.6% in the AP value. Moreover, a rise in both precision and recall by 1.44% and 1.78%, respectively, was noticed. These results could indicate that the inclusion of the pose information did not worsen the detection performed by YOLOv3, and it even improved detection. One possible explanation for this fact could be that the YOLOv3 architecture \"learns\" more localized features in a region and therefore itself is less capable of extracting complex features associated with the pose. However, when the pose adds information to the region in which the object is located, such as fingers, wrist, and forearm, then the object is detected better (i.e., with higher confidence scores). This explanation is consistent with the observations of the results shown in Figure 9.  
Figure 9 shows the detection with YOLOv3 in three test images when training was performed first on the original dataset (top row) and then on the dataset with the pose information (bottom row). In the image on the right side, all objects in the ground truth were detected correctly with and without the pose information. However, when the pose was considered, the confidence score values were higher, especially when the detection box contained pixels associated with the pose. The central top image shows a false positive detection and an undetected handgun (FN) that were correctly detected (central bottom image) when the pose information was included. Finally, in the image on the right, it is shown how the inclusion of the pose (bottom image) allowed detecting the gun that in the raw image without the pose (top image) was undetected (FN). Table 1 includes the results obtained with the only alternative method that considered the pose information ([PERSON]'s method [44], described in Section 2). As shown in the table,  
Figure 9: Results with YOLOv3 trained with the original image (**top**) and when trained with the image overlying the pose information (**bottom**). our method outperformed the results obtained by [PERSON]'s approach. In the context of our challenging dataset, [PERSON]'s method was severely affected by failures in pose detection, as when the body was not fully visible.