# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 1 Introduction  
Due to the limitations of equipment and technology, tramp materials such as gangue, bolt, stick, iron sheet, and iron chain will be mixed into the raw coal in the mining process [1, 2]. These tramp materials mixed in the raw coal can easily cause blockage and scratches of the transfer equipment, and can even cause the failure of the equipment, and in particular, can cause accidents [3]. As the most important solid waste generated during coal mining, gangue will affect the calorific value of coal during the combustion process and cause environmental pollution [4]. Therefore, the automatic and efficient separation of coal and tramp materials is of great significance for ensuring safe production and for improving coal mining efficiency [5]. At present, most coal mines use manual separation to remove tramp materials in raw coal, as shown in Figure 1. This method mainly relies on the manual identification of objects, resulting in a poor working environment, high physical labor intensity, and low productivity, all of which endangers the health of miners, and is not in line with the intelligent development of mines. Other sorting methods, such as wet sorting, will use a lot of water and cause water pollution, while dry sorting has becomea potential direction of sorting technology due to non-contact and economic efficiency. Among them, computer vision-based mineral separation technology has become a hot research topic in recent years [6]. Therefore, detecting tramp materials mixed in raw coal from optical images is an economical and effective method of beneficiation [7; 8; 9]. A tramp material image of raw coal has a rich color, grayscale, texture, shape and contains other characteristic information. However, due to surface pollution, light changes, size differences, and the variety of tramp material shapes, how to extract robust tramp material image features is a challenging task. The detection method based on traditional image processing constructs are featured manually and use support vector machines for classification [10]. This method has high requirements for the detection environment and highly depends on the manual parameter setting, so there is still much room for improvement in the generalization effect and stability of the detection [11; 12; 13]. On the other hand, the object detection technology based on deep learning improves the performance of the detector by learning from training data and by adaptively extracting stable image resources [14]. Previous research has focused on the detection of gangue, ignoring the detection of other types of tramp materials, especially small targets. In this research, we designed a object detection algorithm for multiple tramp materials in raw coal. To ensure real-time target detection performance, we used a structure similar to SSD [15]. SSD uses a multi-scale feature map strategy to detect objects of different sizes in real time. However, because there is no semantic interaction between shallow and deep feature maps, small targets cannot be detected effectively. For this reason, SSD is not suitable for detecting multi-scale tramp material targets. To improve the performance of the detector, we first used a densely connected network containing a four-stage feature extractor to stably down-sample the feature map. The feature extraction module adopts a multi-branch dilation convolution structure to realize the efficient use of feature images by fusing feature images of different receptive fields. The cross-layer feature fusion and the attention fusion module are used in the feature fusion module to fuse the position information of the shallow feature map and the semantic information of the deep feature map to improve the detection ability of small objects. We refer to the proposed detector as the tramp materials in raw coal single  
Figure 1: Scene depicting manual selection. shot detector (TMRC-SSD). The main contributions of this research can be summarized as follows:  
1.