Dental bur detection system based on asymmetric double convolution and adaptive feature fusion

This study aims to improve the detection of dental burs, which are often undetected due to their minuscule size, slender profile, and substantial manufacturing output. The present study introduces You Only Look Once-Dental bur (YOLO-DB), an innovative deep learning-driven methodology for the accurate detection and counting of dental burs. A Lightweight Asymmetric Dual Convolution module (LADC) was devised to diminish the detrimental effects of extraneous features on the model's precision, thereby enhancing the feature extraction network. Moreover, to augment the efficiency of feature integration and diminish computational demands, a novel fusion network combining SlimNeck with BiFPN-Concat was introduced, effectively merging superficial spatial details with profound semantic features. A specialized platform was developed for the detection and counting of dental burs, and rigorous experimental assessments were performed. Promising results were achieved. YOLO-DB yielded a Mean Average Precision (mAP@0.5) of 99.3% on the dental bur dataset, with a notable 3.2% increase in mAP@0.5:0.95 and a sustained detection pace of 128 frames per second. The model also achieved a 14.4% reduction in parameter volume and a 17.9% decrease in computational expenditure, while achieving a flawless counting accuracy of 100%. Our approach outperforms current detection algorithms in terms of detection capability and efficiency, presenting a new method for the precise detection and counting of elongated objects such as dental burs.