## 3 Proposed Model
### Hub Object Detection  
There are various problems in computer vision and image processing [36; 37; 38; 39; 40]. Object detection, as one of the most applicable problems in computer vision, is the process of categorizing especial objects such as cars, pedestrian, bicycles, traffic signs, faces, etc. [36; 37]. Similar to many computer vision tasks, related algorithms can be divided into two categories: feature-based object detection [41] and Deep learning (DL) object detection (OD) [42]. DL is considered a special type of machine learning (ML). Both ML and DL commence with two training and test data with a model to probe the weights that make the model best fit the data [36; 43; 44; 45]. Nowadays, deep learning has experienced a sharp adoption rate in wide applications [7; 36; 44]. It can be used in emerging traffic intelligent services, self-driving cars, image retrieval, security, sports teams analysis to generate heat maps for players, crowd video analysis, implementation of intelligent quality control (QC) systems [36; 37; 45; 46] for smart manufacturing, etc. In [47], the authors presented an innovative algorithm called Single Shot Multi-Box Detector (SSD) to implement object detection using a single deep neural network combining regional proposals and feature extraction. It discretizes bounding boxes output space into a set of default boxes over different aspect ratios and scales per feature map location. This set is used and applied to the feature maps. Since they are computed by passing an image through an image classification network, the feature extraction for the bounding boxes can be extracted in just a single step. Scores are generated for each object category in every of the default bounding boxes. In order to better fit the ground truth boxes, adjustment offsets are calculated for each box. In addition, this network merges all predictions obtained from several feature maps with divers resolutions to handle large number of objects with various sizes. In fact, SSD strongly benefits from multi-scale feature maps, which are generated from a backbone network such as VGG-16 [48] to detect objects having various sizes. The SSD fully eliminates proposal generation and subsequent pixel or feature resampling stages. It also encapsulates all computations in just one network. As a result, it is easy to train SSD and easy to integrate into other systems. Experimental outcomes on the ILSVRC, PASCAL VOC, and COCO datasets verify that in comparison to methods that utilize some additional object proposal steps, the SSD not only has  
Figure 3: The hub and a part of one blade in a sample wind turbine tower image. competitive accuracy but also has much lower runtime while providing a consistent framework for training and inference. Figure 4 illustrates SSD architecture [47]. Now, SSD is one of the top object detection algorithms in both aspects, accuracy and speed [49]. In [49], how to use feature maps effectively to improve the performance of the conventional SSD was proposed and analyzed. In this work, we deploy the special version of SSD which is called SSD7. It is a smaller 7-layer version that can be trained from scratch relatively quickly, even with an ordinary GPU, while it is capable enough to solve less complex object detection tasks. The idea is just to build a low-complexity network that is fast for our purposes. Table 1 shows some primary configuration of our trained model. The first step of our algorithm is the hub detection. We propose to use SSD7 object detection to localize a hub object in the input image. Figure 5 depicts some selected hubs and blades from our dataset. Figure 4: Single Shot Multi-Box Detector (SSD) architecture [47]. Figure 5: Some selected samples of hubs and blades images at original sizes from our dataset: hubs (front view and back view), blades ( near zones and far zones). Selecting the Best Object  
Usually, the object detection algorithm proposes several candidates, which should be filtered to select the best one.