# FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection
## 1 Introduction  
Target detection is one of the main tasks of computer vision, and it is extensively used in areas such as driverless cars, face recognition, road detection, medical image processing, and human-computer interaction. The traditional target detection methods such as Local Binary Patterns (LBP) [1], Scale Invariant Feature Transforms (SIFT) [2], Histograms of Oriented Gradient (HOG) [3], and Haar-like (Haar) [4], are based on hand-crafted features. This feature extracted by the traditional target detection methods has obvious limitations. Firstly, the feature extraction is complex and the calculation speed is slow. Secondly, the artificial features largely limit the application scenarios of the algorithm. It is difficult to satisfy the needs of real-time detection on a complex and large dataset.  
In recent years, a lot of target detection algorithms based on the convolutional neural network (CNN) have been proposed to solve the problem of poor accuracy and real-time performance of commonly used traditional target detection algorithms. Target detection algorithms based on convolutional neural networks have been divided into two categories according to the number of feature layers extracted from different scales. The first is the single scale characteristic detector type, such as region with CNN feature (R-CNN) [5], Fast Region-based Convolutional Network method (Fast R-CNN) [6], Faster R-CNN [7], Spatial Pyramid Pooling Networks (SPP-NET) [8], and You Only Look Once (YOLO) [9], and the other is the multi-scale characteristic detector type such as Single Shot Multibox Detector (SSD) [10], Deconvolutional Single Shot Detector (DSSD) [11], Feature Pyramid Networks (FPN) [12], and Feature-Fusion SSD (FSSD) [13]. The former type detects targets of different sizes under a single scale feature, which is a limitation to detection of targets that are too large or too small; the latter type extracts features from different scale feature layers for target classification and location, which improves the detection effect.  
Among various target detection methods, SSD is relatively fast and accurate because it uses multiple convolution layers of different scales for target detection. SSD takes the Visual Geometry Group(VGG16) [14] as the basic network, and adopts a pyramid structure feature layer group (multi-scale feature layer) for classification and positioning. It uses features extracted from shallow networks to detect smaller targets, and larger targets are detected by deeper networks features. However, SSD does not consider the relationships between the different layers so that semantic information in different layers is not taken full advantage of. It might cause the problem named \"Box-in-Box\" [15], which means that a single target is detected by two overlapping boxes. In addition, the feature semantic information extraction by shallow networks is less and might not have enough capability to detect small targets.  
To solve these problems, we propose a Single Shot Object Detection with Feature Enhancement and Fusion (FFESSD) by adding the Feature Fusion Module and Feature Enhancement Module to the conventional SSD. Our network can achieve 81.8% mAP on the PASCAL VOC 2007 test [16].  
The contributions of our work can be summarized as follows:  
1. To fuse feature information of each layer, we propose a new fusion mechanism referred to as the Feature Fusion Module.
2. A pair of novel Feature Enhancement Modules are proposed to enhance the extraction of semantic information.
3. We show that FFESSD achieves state-of-the-art results on the Pascal VOC at a real time processing speed, and the performance of FFESSD in fuzzy target detection is better than the conventional SSD.