# A Deep Learning-Based Approach for Extraction of Positioning Feature Points in Lifting Holes
###### Abstract  
Due to uncontrollable influences of the manufacturing process and different construction environments, there are significant challenges to extracting accurate positioning points for the lifting holes in prefabricated beams. In this study, we propose a two-stage feature detection, which comprises the ADD (multi-Attention DASPP DeepLabV3+) model and the VLFGM (Voting mechanism line fitting based on Gaussian mixture model) method. Initially, the YoloV5s model is employed for image coarse localization to reduce the impacts of background noise, and the ADD model follows to segment the target region. Then, the multi-step ECA mechanism is introduced to the ADD. It can mitigate the loss of interest features in the pooling layer of the backbone as well as retain the details of the original features; DASPP is adopted to fuse features at different scales to enhance the correlation of features among channels. Finally, VLFGM is utilized to reduce the dependency of accuracy on segmentation results. The experimental results demonstrate that the proposed model achieves a mean intersection over union (mIoU) of 95.07%, with a 3.48% improvement and a mean pixel accuracy (mPA) of 99.16% on the validation set. The improved method reduces vertexes error by 30.00% (to 5.39 pixels) and centroid error by 28.93% (to 1.72 pixels), which exhibits superior stability and accuracy. This paper provides a reliable solution for visual positioning of prefabricated beams in complex environments. alignment feature points; DeepLabV3+; line fitting; visual lifting; YoloV5s +
Footnote †: journal: Medical Imaging  
of the motor based on the three-dimensional coordinates to achieve alignment assembly. However, using BV for alignment requires highly overlapping corresponding feature points and the acquisition of the target's centroid position. Nevertheless, corner detection cannot provide centroid points, and it is more commonly used to solve the centroid through the target connected domain. The prerequisite for obtaining the interest connected domain is to separate the hoisting hole area and background information in the image. However, the environment at the bridge lifting site is complex, and there may be factors such as lighting, shadows, and foreign matter within the target region. Traditional segmentation methods are easily influenced by these noise factors. DeepLabV3+ is a popular and effective pixel-level semantic segmentation model [10], even used to determine positioning points [11; 12]. However, it has limitations in accurately identifying objects in large-scale scenes. To overcome this, researchers have introduced object recognition as a pre-processing step to minimize background interference. [PERSON] [13] and [PERSON] [14] both adopted Yolo series models to address the segmentation of small objects. Additionally, morphological operations and geometric constraints are employed to refine the segmented images, resulting in more realistic representations of fine details. Furthermore, many studies use different methods to enhance the effect of edge segmentation, including using multi-level cascading residual modules to increase the range of the backbone receptive field [15], introducing the dense connection method in the ASPP module, reducing the spacing of feature sampling pixels [16], using improved convolution methods [17] and contour constraint methods [18]. Feature point detection methods for image salient features include edge-based corner detection methods and line feature-based detection methods [19]. However, these methods are often based on pixel gradients and are prone to noise points due to the influence of image texture. In order to meet the needs of different scenarios, [PERSON] [20] improved the CenterNet model to obtain target alignment feature points, but this method used for perspective view positioning can cause significant errors. [PERSON] [21] adopted the FCN model to segment the target area, using the pixel with the highest probability of category within the segmented area as the matching point.