Nowadays, artificial intelligence plays a crucial role in the automotive industry. It is used as machine learning, deep learning, neural networks, natural language processing, computer vision, fuzzy logic and other techniques to improve efficiency, speed and comfort. More and more popularity gains YOLO in the automotive industry that demonstrates outstanding performance and accuracy. However, efficiency of the latest versions, namely YOLOv11 and YOLO26, is underexplored. This article addresses this gap. Moreover, the influence of layer freezing technique on YOLOv11 and YOLO26 during detection and segmentation of different car parts is investigated, that is very essential in applications such as safety analysis, damage assessment, insurance and automated production. Experiments were conducted in Google Colaboratory Pro environment with GPU NVIDIA A100 (40Gb). Carparts Segmentation Dataset was used for experiments, containing 3833 labeled images split into three subsets: training, validation and test. Models were trained for 100 epochs with an early stopping mechanism (patience 20) to avoid overfitting, batch size 16, optimizer AdamW. It was found empirically that YOLOv11 outperform
📖 افتح في inklap 🔗 DOI 📮 اطلب بحثاً