Focus DETR: Focus detection transformer for ship wall-climbing robot real-time object detection

With the growing use of wall-climbing robots for ship paint removal in repair yards, real-time detection of the targets of the wall-climbing robots and accurate positioning of them has become a very important task. Due to the changes in the external environment and the diversity of working postures of the wall-climbing robot, maintaining stable detection accuracy and real-time performance is a challenging task. To address this issue, a focus detection transformer (Focus-DETR) architecture is proposed. Firstly, the spatial attention recursive gated convolution(Sa-Gn) module is employed in the final stage of the backbone to extract high-level features, not only gaining high accuracy but also maintaining real-time speed. Secondly, to improve the ability to extract key features of the wall-climbing robot object, a hybrid encoder method is introduced to integrate features from adjacent stages of the neck. What’s more, the focal EIOU loss function in the detection head optimizes the width and height errors of the detection box and adjusts its loss weight. It not only improves the alignment accuracy of the detection center point of the object bounding box of the wall-climbing robot, but also is suitable for lightweight application deployment. Experimental results show that compared with RT-DETR-R50, Focus DETR-S improves the detection mAP by 6.0 % on the wall-climbing robot dataset, with the inference speed very close to that of RT-DETR-R50. It has also achieved the same performance improvement on the UAVDT dataset.