# TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
## 1 Introduction  
When people glance at an image, they can immediately know what the objects are, which type of the targets are in the image, and where they are [1]. In order to match the excellent human visual system, fast and accurate object detection represents a significant focus of research in the field of computer vision. Their achievements in this area are used in various applications, such as video surveillance, face recognition, human-computer interaction, and self-driving technology, to name just a few [2]. Robust object detection in a simple environment is relatively easy to achieve, but it is hard to guarantee both speed and accuracy of recognition in practice since the real-world environments may look more complex [3]. Deep learning techniques have been widely employed in the field of object detection during the past decade and have become efficient approaches of extracting features from images. Generic object detection based on deep learning is characterized by two factors: plentiful features and robust feature representation capabilities. They are also combined with traditional hand-crafted features [4]. Existing object detection methods based on deep learning can be generally grouped into two categories, the models based on region proposals and the models based on regression [5]. Currently, classical object detection methods based on region proposals include Region-based Convolutional Neural Networks (R-CNNs) [6], Spatial Pyramid Pooling Networks (SPP-net) [7], Fast R-CNNs [8], Faster R-CNNs [9], and Region-based Fully Convolutional Networks (R-FPN) [10]. However, these approaches fail to achieve real-time speed due to the expensive running process and inefficiency of region propositions. The R-CNNs hypothesizes object locations which depends on region proposal algorithms. Features in the R-CNN are, first, extracted from each candidate region, and then fed into convolutional neural networks. Finally, they are evaluated by Support Vector Machines(SVM) [11]. The computational cost of R-CNNs is dramatically reduced by sharing convolutions across proposal [12]. Recent advances, for instance, SPP-net and Fast R-CNN tend to reduce the training time. However, time-consuming proposal computation is a bottleneck [13]. To this end, a Region Proposal Network (RPN) is proposed, which shares full-image convolutional features with object detection networks [14], as a kind of fully convolutional network (FCN), which can efficiently predict regions with various scales and aspect ratios. The Faster R-CNN is developed by emerging the RPN and the Fast R-CNN into a single network, in which the convolutional features are shared with the down-stream detection network. It achieves nearly real-time rates by using very deep networks and sharing convolutions at test-time, and it can be trained end-to-end for generating detection proposal [15]. Typical object detection models based on regression are You Only Look Once (YOLO) [1] and Single Shot Multi-box Detector (SSD) [16]. As a single neural network, YOLO is extremely simple, which can concurrently predict bounding boxes coordinates and associated class probabilities. Besides, YOLO frame detection is regarded as a regression problem, and it achieves end-to-end target detection without complex pipeline [1], which results in high efficiency. Furthermore, YOLO achieves a higher mean average precision (mAP) than other real-time systems [17]. The SSD detects objects using a single deep neural network, which only needs input images and ground truth boxes for each object in the training process. Based on a feed-forward convolutional network, SSD generates a fixed-size collection of bounding boxes as well as scores of class probabilities. After a non-maximum suppression step, SSD produces the final detections [18]. In general, the SSD has relatively better accuracy than YOLO. Nevertheless, both YOLO and SSD fail to perform well on small target detection, partly because the target may not have enough information to learn from the structures at the very deep convolutional layers [19].