# Automatic Asbestos Control Using Deep Learning Based Computer Vision System +
## 2 Description of the Experimental System and Algorithm of Its Work
### Algorithm of the System Work
The dataset \(N1\) consists of 46 images of alone stones obtained in the laboratory conditions. The image size is \(5184\times 3456\). The typical image and its labelled mask of rock and veins are shown in Figure 4a-c, correspondingly. For the dataset \(N2\) 134 images of rock chunks were taken on the fixed conveyor belt of the asbestos processing factory under the fixed artificial lighting [6]. The image size is \(2592\times 2048\). Each image contains about 10-20 rock chunks. Images are taken for 50 selected samples with the laboratory-measured asbestos content value. For each sample, 2-3 pictures were taken with the random shuffled position of stones (134 images in total). The typical images and their labelled mask of rock and veins are shown in Figure 5a-c, correspondingly. The dataset \(N3\) consists of 69 images that were taken for 6 open pits with manual aiming in sunny weather in autumn. The image size is 2592 \(\times\) 2048. Each image contains one rock chunk selected by a geological specialist as typical corresponding to open pits. Typical images and their labelled mask of rock and veins are shown in Figure 6a-c, correspondingly. The dataset \(N4\) consists of 984 images that were taken for 10 open pits selected by the geological specialist. The image size is 2592 \(\times\) 2048. Some of the places were too long for one picture and thus were divided into parts (with taken separate images for each of the

Figure 4: The typical image (**a**) and labelled mask of alone rock chunks boundaries (**b**) and veins (**c**) for the dataset \(N1\). Figure 5: The typical image (**a**), its labelled mask of stone boundaries (**b**), and veins (**c**) for the dataset \(N2\). Figure 6: The typical image (**a**), its labelled mask of alone rock chunks boundaries (**b**), and veins (**c**) for the dataset \(N3\). parts). There were 22 such parts in the dataset \(N\)4 at all. Each image contains one rock chunk selected by the automatic-aiming algorithm. The images were obtained in different weather in autumn and winter including sunny, cloudy, rainy and snowy weather. Typical image and its labelled mask of rock and veins are shown in Figure 7a-c, correspondingly. The dataset \(N\)5 consists of 56 images of the whole open pit. The image size is \(2592\times 2048\). The dataset was collected for solving the rock chunks detection problem. A typical image with the ground truth mask of rock chunks is shown in Figure 8.

The summary of information about collected datasets and their features is shown in Table 1.

Figure 8: Typical image with the ground truth mask of rock chunks. Figure 7: The typical image (**a**), its labelled mask of alone rock chunks boundaries (**b**), and veins (**c**) for the dataset \(N\)4. ## 4 CNN Architectures and Training Details

### Rock Chunks Detection

The problem of the rock chunks detection on the open pit picture was solved as the instance segmentation task. The first reason for this is to evaluate the area of each stone for stage 4 of the presented scheme of the system work. In addition, it was necessary to estimate both coordinates of chunks and their area distribution by the requirement of the geological service. The Mask-R-CNN [16] based network was applied for this. The Mask-R-CNN was implemented using the ResNet-50 architecture [23] as the feature encoder (backbone) in combination with the feature pyramid network [24]. The connections of the ResNet-50 with the feature pyramid network were implemented by the so-called lateral connection [24] for blocks with \(256,512,1024,2048\) feature maps. After the feature encoder, the convolution layer with the kernel \(3\times 3\) was applied to reduce the aliasing effect of upsampling and to reduce the feature map number to 256 for each block. Thus, it was possible to make decisions about each point based on 256 features. For classification, the region proposal network (RPN) was applied consistently [23]. The training and inference were selected to be the same as in the original work [23].