The increasing generation of fines and residues in the steel industry, as well as the constant environmental concern with the emission of gases (CO \(_{2}\) ) and the large volume of waste generated in iron ore processing plants and the need to dispose of them properly, has motivated the study of technological alternatives that allow the reuse and reprocessing of these materials. A viable alternative is to reintroduce it into the iron and steel manufacturing process itself, through agglomeration techniques, consolidating briquetting as one of the most suitable technologies for using fine ore, coal and waste from the sector, in an economical and environmentally friendly way. This work aimed to develop a computer vision system using artificial intelligence for the detection and classification of briquettes in a mining company in the state of Espírito Santo, addressing fundamental concepts of the process and presenting a detailed analysis of the main articles and related works in the area of computer vision applied to the steel industry. YOLOv11 was used as a model for the detection and classification of briquettes and obtained a mAP of 0.84, thus demonstrating its efficiency and accuracy for this process.