CAGNet: A Multi-Scale Convolutional Attention Method for Glass Detection Based on Transformer

Glass plays a vital role in several fields, making its accurate detection crucial. Proper prevents misjudgments, reduces noise from reflections, and ensures optimal performance other computer vision tasks. However, the prevalent usage of glass daily applications poses unique challenges for vision. This study introduces novel convolutional attention segmentation network (CAGNet) predicated on transformer architecture customized image detection. Based foundation our prior study, CAGNet minimizes number training cycles iterations, resulting enhanced efficiency. is built upon strategic design integration two types mechanisms coupled with head applied comprehensive feature analysis fusion. To further augment precision, incorporates custom edge-weighting scheme to optimize within images. Comparative studies rigorous testing demonstrate that outperforms leading methodologies detection, exhibiting robustness across diverse range conditions. Specifically, IOU metric improves by 0.26% compared previous presents 0.92% enhancement over those state-of-the-art methods.