###### Abstract  
Advances in machine learning algorithms have allowed object detection and classification to become booming areas. The detection of objects, such as rocks, in mining operations is affected by fog, snow, suspended particles, and high lighting. These environmental conditions can stop the development of mining work, which entails a considerable increase in operating costs. It is vital to select a machine learning algorithm that is accurate, fast, and contributes to lower operational costs because of the aforementioned environmental situations. In this study, the Viola-Jones algorithm, Aggregate Channel Features (ACF), Faster Regions with Convolutional Neural Networks (Faster R-CNN), Single-Shot Detector (SSD), and You Only Look Once (YOLO) version 4 were analyzed, considering the precision metrics, recall, AP\({}_{50}\), and average detection time. In our preliminary tests, we have observed that the differences between YOLO v4 and the latest versions are not substantial for the specific problem of rock detection addressed in our article. Therefore, YOLO v4 is an appropriate and representative choice for evaluating the effectiveness of existing methods in our study. The YOLO v4 algorithm performed the best overall, whereas the SSD algorithm performed the fastest. The results indicate that the YOLO v4 algorithm is a promising candidate for detecting rocks with visual contamination in mining operations. mining environments; rock detection; visual contamination; machine learning algorithms; deep learning techniques +
Footnote †: journal: Medical Imaging  
0000-0001-6000-0000-0000]R.R. advantage of this model is that it regards training as a suitable method to decompose an object for learning and detection as an ensemble of detections of different parts of the object. Multiple kernels were proposed in [6] for object detection; in this sense, a three-stage classifier was developed by combining linear, quasi-linear, and nonlinear SVM kernels. Object detection [7] was proposed by measuring the saliency of a window sliding over an image. This window saliency is defined as the cost of composing a window using the remaining parts of the image. Because they use the complete image as context, a good generalization of objects and complex backgrounds is achieved. In [8], they modify an optimization algorithm called Grey Wolf to reduce calculation complexity and evaluate petrophysical properties. The rebirth of deep learning began in 2012 [9]. These networks can create robust high-level representations of images. Deep learning-based object detectors highlight two types of architectures: single-stage and two-stage architectures. Single-stage architectures are faster than two-stage architectures, but they tend to be less accurate. Among the most prominent are the Single-Shot Detector (SSD) [10] and You Only Look Once (YOLO) [11]. These architectures revolutionized single-stage detectors as they are fast and achieve accuracy levels similar to those of two-stage detectors. Various variants of these architectures have been developed. YOLO was modified in [12] to be used in embedded devices, and in [13], the authors incorporated a meta-algorithm into SSD to improve learning with little data. Regions with a Convolutional Neural Network (R-CNN) family can be found among two-stage architectures. These deep networks have become so widespread that there are few areas where they are yet to be applied. In agriculture, they have been used to detect diseases in apples [14] and tomatoes [15], locate harmful insects [16], detect fruits [17], and identify defects in bamboo [18]. Another significant area in which many advances have been made is medicine. Most research has focused on semantic segmentation; however, some investigations have been conducted to detect structures. A deep learning algorithm was used to detect and classify oral tumors [19]. An analysis of state-of-the-art network architectures for the detection and diagnosis of cervical cancer was performed in [20].