# TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
## 4 Experimental Verification and Result Analysis
### Comparison of Speed and Precision  
For this test, a total of 500 pictures containing 10 types of objects were selected in NWPU VHR-10 dataset. The detailed 10 categories are airplane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, and vehicle. Cross validation is used to evaluate the precision of TF-YOLO. During the process of data pre-processing, nine cluster centers are selected, and the targets are distributed into three scales by K-means++. The size of these nine scales are arranged from small to large as follows: (22,19), (46,29), (39,54), (86,52), (71,108), (139,86), (106,161), (231,130), and (289,188). Meanwhile, a total of 300 images containing small and dense objects were selected in VOC 2007 dataset. The samples, based on their accurate category and prediction class, can be divided into the following four categories [35]: TP (true positive), FP (fault positive), TN (true negative), and FN (fault negative). Precision refers to the proportion of TP in the predicted positive example, and recall refers to the proportion of TP in the truly positive example. They can indicate the number of their corresponding samples. The accuracy and recall rate are defined as follows:  
\[\text{precision }=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{recall }=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
Generally, the trade-off between accuracy and recall is a tricky problem. In order to evaluate the precision among different types of targets, mAP is introduced, which is one of significant measure metrics to evaluate the test results [36]. Meanwhile, in order to maintain the consistency of the data distribution in each subset, the feature can be extracted through hierarchical sampling layers. AP and mAP of the three sets of comparative experiments are illustrated in Table 2. The first set of comparison experiments is the YOLOv3-tiny network. Inspired by YOLOv3-tiny, the second set of comparison experiments is defined as the YOLO_k network. Without data pre-processing, its structure is the same as TF-YOLO. The third group of comparative experiments is the TF-YOLO network, which improves the size of anchors through K-means clustering. One significant advantage of the proposed TF-YOLO network is real-time working on portable devices. For instance, the TF-YOLO network can be applied to the embedded system on the Nvidia Jetson TX2. After training models in NWPU VHR-10, the TF-YOLO network runs at about 24.3 FPS. However, the YOLOv3-tiny network runs at 24.6 FPS. In embedded systems, the YOLOv3-tiny network is slightly faster than the TF-YOLO network. Partly because the network in TF-YOLO is deeper than YOLOv3-tiny, and more parameters in TF-YOLO are learnt during the training period. However, the accurate of the TF-YOLO network is significantly improved than the YOLOv3-tiny network, which results in the TF-YOLO network performing is well on detecting multi-scale targets with real-time speed. As obviously revealed in Table 2, the mAPs of TF-YOLO network are prominently higher than those of the YOLOv3-tiny and YOLO_k networks, regardless of whether complex objects are included. The YOLOv3-tiny network does not respond satisfactory to the targets in testing images. Besides, by improving the network structure in the YOLO_k network, mAP significantly improves to 0.86413, but it is still unsatisfactory. On the other hand, the proposed TF-YOLO network, within K-means++ clustering to change anchors, demonstrates the highest AP of the single-class target and achieves 0.89449 in mAP. In VOC 2007 dataset, a total of 200 images containing small targets were selected. In consideration of the detection results, the TF-YOLO and YOLOv3-tiny networks were chosen for comparison with state-of-art methods based on region proposals, including SPP-net, RCNN, Faster RCNN, and YOLOv3. The APs and mAPs of the above methods are displayed in Table 3.  
Table 3 shows that the AP scores of the TF-YOLO method are higher than those of the classical methods in every object class.