# AIR-YOLOv3: Aerial Infrared Pedestrian Detection via an Improved YOLOv3 with Network Pruning
###### Abstract  
Aerial object detection acts a pivotal role in searching and tracking applications. However, the large model, limited memory, and computing power of embedded devices restrict aerial pedestrian detection algorithms' deployment on the UAV (unmanned aerial vehicle) platform. In this paper, an innovative method of aerial infrared YOLO (AIR-YOLOv3) is proposed, which combines network pruning and the YOLOv3 method. Firstly, to achieve a more appropriate number and size of the prior boxes, the prior boxes are reclustered. Then, to accelerate the inference speed on the premise of ensuring the detection accuracy, we introduced Smooth-L1 regularization on channel scale factors, and we pruned the channels and layers with less feature information to obtain a pruned YOLOv3 model. Meanwhile, we proposed the self-built aerial infrared dataset and designed ablation experiments to perform model evaluation well. Experimental results show that the AP (average precision) of AIR-YOLOv3 is 91.5% and the model size is 10.7 MB (megabyte). Compared to the original YOLOv3, its model volume compressed by 228.7 MB, nearly 95.5 %, while the model AP decreased by only 1.7%. The calculation amount is reduced by about 2/3, and the inference speed on the airborne TX2 has been increased from 3.7 FPS (frames per second) to 8 FPS.  
infrared imaging: aerial object detection; human detection; prune; YOLO +
Footnote †: journal: MedicalIn this paper, an improved aerial infrared aerial YOLOv3 (AIR-YOLOv3) is proposed based on model pruning. The overall block diagram of the system is shown in Figure 1. Aerial infrared pedestrian data were collected through self-built tracking drones and were manually cleaned and marked. According to aerial data characteristics, the prior boxes of object are reclustered, and a more appropriate number and size of prior boxes are reselected. Based on the YOLOv3 model [9], we introduced the Smooth-L1 regularization on scale factor \(\gamma\) to sparse the model, and pruned channels and layers with small scale factor \(\gamma\). The model is deployed on the airborne NVIDIA Jetson TX2.  
Contributions and innovations of this paper are summarized as follows:  
* An improved YOLOv3, AIR-YOLOv3, is proposed, which learns a more compact network structure by pruning [7], and it achieves a good trade-off between detection accuracy and inference speed.
* Since few published infrared-based aerial pedestrian datasets could be accessed, we constructed a new infrared aerial dataset, named the AIR-pedestrian dataset, that includes sample images in different scenarios.
* A UAV system was designed, and all the algorithms were implemented on the onboard Jetson TX2. The experimental results show that our proposed method can detect infrared pedestrians in public areas effectively.  
The rest of this paper is organized as follows. This paper discusses related work in Section 2 below. Our proposed AIR-YOLOv3 method will be fully introduced in Section 3. Section 4 describes the experimental details. Finally, a short conclusion and discussion of this work are summarized in Section 5.