Development of an Object Detection Classification Model Using the R-CNN Method to Measure the Quality of Biomass Briquettes

Abstract

Biomass briquettes are increasingly recognized as a promising source of renewable energy, as they are produced from organic waste materials such as agricultural residues, wood scraps, and coconut shells. As an environmentally friendly energy alternative, ensuring their quality is essential to achieve consistent combustion and energy efficiency. Previous studies have explored the evaluation and classification of briquettes using image-based machine learning techniques. One study applied a Convolutional Neural Network (CNN) to distinguish good from bad briquettes, achieving very high performance, with accuracy, precision, and recall reportedly reaching up to 100%. However, these results were obtained using a dataset of briquette images captured on a plain white background with minimal variation. When tested on more complex data images of briquettes against non-uniform backgrounds containing multiple objects, the CNN model’s performance dropped drastically. Under these real-world conditions, accuracy and precision fell below 50%, indicating that the model lacked robustness for practical applications. To overcome this limitation, further research focused on developing an improved classification model using the Region-Based Convolutional Neural Network (R-CNN) method. R-CNN employs a region proposal algorithm using a selective search approach, enhancing object detection and localization in cluttered backgrounds. Experimental testing through a developed interface demonstrated a significant improvement, with accuracy increasing to approximately 75%. These results suggest that the R-CNN method provides a more reliable and practical solution for classifying briquette quality, particularly in the presence of complex image backgrounds, highlighting the importance of advanced object detection techniques for real-world applications.