# Autonomous Multiple Tramp Materials Detection in Raw Coal Using Single-Shot Feature Fusion Detector
## 2 Related Works  
### Image-Based Detection for Extraction of Tramp Materials in Raw Coal  
Because it does not need to use water, dry coal preparation technology has advantages in environmental protection and economy, and has become a hot research field. The method based on radiation [16] requires the careful management of radiation sources to avoid harm to the human body. The method based on density [9] measurement is complicated and requires materials to be arranged neatly. The image-based detection method is safer and more convenient than the above-mentioned methods. Traditional image processing and classic machine learning algorithms can use the image's grayscale [17], texture [18], fractal dimension [19; 20], morphology [8] and spectral feature [21], by constructing a feature vector to identify tramp material objects represented by gangue. However, this approach relies too much on human experience judgment and prior knowledge, which has certain limitations. With the rise of deep learning technology represented by Convolutional Neural Networks (CNN), a new technical approach is provided for image recognition of tramp material objects. CNN adopts an end-to-end training method, which can adaptively extract robust image features, thereby avoiding the subjectivity of manually extracting features. [PERSON] [22] proposed a simple LeNet-5 model, which realizes the automatic classification of coal and gangue images. [PERSON] [23] uses transfer learning strategies to train small sample datasets to improve recognition accuracy. According to the difference between thermal images of coal and gangue, [PERSON] [24] proposed a recognition model based on CNN. [PERSON] [25] proposed a method to identify coal gangue using the intensity image of lidar echo and DenseNet, the recognition rate reached being 93%. However, the above four studies only explore the recognition of coal and gangue images, and could not achieve the detection tasks of target positioning and other various types of tramp materials. [PERSON] [26] proposed a full convolutional network based on U-Net to achieve pixel-level segmentation of coal and gangue in images. Nonetheless, due to the limitations of the dataset size and type, detection of other types of tramp materials such as bolt cannot be achieved. [PERSON] [27] proposed a CG-YOLO dynamic target detection method and tested the sorting effect of supporting robots at different speeds, which can provide a solution for the realization of mechanical sorting execution unit after the completion of tramp material object detection. [PERSON] [28] integrates the method of a multi-channel feature fusion layer and optimization loss function into the cascade network to improve the identification accuracy. Although experiments and visualizations show that this method is effective for the detection of coal and gangue, other types of tramp materials on the conveyor belt are not fully considered. Through the continuous in-depth research of many scholars, the image-based detection of tramp materials in raw coal is a feasible technical approach and has potential.