# The Application of Improved YOLO V3 in Multi-Scale Target Detection
## 1 Introduction  
Target detection is one of the research hotspots in the field of computer vision. The location and the category of the targets can be determined by using target detection. Nowadays, target detection has been applied in many fields such as military and civil areas, including image segmentation [1; 2], intelligent surveillance [3; 4; 5], autonomous driving [6; 7], and intelligent transportation [8; 9]. With the rapid development of graphics processing unit (GPU) hardware, deep learning has made significant progress [10] and a lot of target detection algorithms based on deep learning have been used in our daily life [11], such as pedestrian detection [12], face detection [13; 14; 15], and vehicle detection [16; 17]. The convolutional neural network (CNN) is a kind of network with many layers used to extract features based on invariance of regional statistics in images [18]. By training on the dataset, CNN can learn the features of the targets that need to be detected autonomously and the performance of the model can be improved gradually. With the improvement of computer hardware, the structure of CNN becomes much deeper and the features of the targets can be better learned. The conventional LeNet [19] consisted of five layers and the number of the layers in Highway Networks [20] and ResidualNetworks [21] has surpassed 100. DenseNet [22] connects layers in a feed-forward fashion, which is useful for reusing the features and avoiding gradient fading. The state-of-the-art target detection algorithms based on CNN can be divided into two categories. The first category is two stage target detection algorithms, such as R-CNN [2], Fast R-CNN [23], Faster R-CNN [24], Mask R-CNN [25], etc. The target detection process is divided into two phases by these algorithms. Firstly they use a Region Proposal Network (RPN) to generate a sparse of candidate anchor boxes and then the location and category of the candidate targets can be predicted and identified by the detection network. These algorithms can achieve perfect performance for target detection. However, they are not end-to-end target detection algorithms. The second category is one stage target detection algorithms, such as OverFeat [26], SSD [27], YOLO [28], YOLO 9000 [29], YOLO V3 [30], You Only Look Twice [31], etc. There is no need to use RPN to generate the candidate targets. These algorithms predict the target location and category information directly through the network. They are end-to-end target detection algorithms. Therefore, the speed of the one stage target detection algorithm is faster. As a representative algorithm of one stage target detection, YOLO V3 [30] predicts targets of different size at 3 different scales, which uses a similar concept to feature pyramid networks [32]. Instead of choosing anchor boxes by hand, YOLO V3 runs K-means clustering on the dataset to find good priors automatically. However, it divides up the priors evenly across scales, which may cause some anchor boxes to be placed at inappropriate scales because the clusters are arbitrarily allocated. In this paper, a multi-scale target detection approach based on YOLO V3 is proposed. To improve the detection performance of the targets with different sizes, we extend the detection scale of YOLO V3 from 3 to 4. To avoid gradient fading and enhance the reuse of the features, the six convolutional layers in front of the output detection of each scale are transformed into two residual units. To select appropriate size of the anchor boxes for each scale, a mathematical derivation method based on Intersection over Union (IOU) is proposed.