Adaptive cropping shallow attention network for defect detection of bridge girder steel using unmanned aerial vehicle images

Bridges are an important part of railway infrastructure and need regular inspection and maintenance.Using unmanned aerial vehicle(UAV)technology to inspect railway infrastructure is an active research issue.However,due to the large size of UAV images,flight distance,and height changes,the object scale changes dramatically.At the same time,the elements of interest in railway bridges,such as bolts and corrosion,are small and dense objects,and the sample data set is seriously unbalanced,posing great challenges to the accurate detection of defects.In this paper,an adaptive cropping shallow attention network(ACSANet)is proposed,which includes an adaptive cropping strategy for large UAV images and a shallow attention network for small object detection in limited samples.To enhance the accuracy and generalization of the model,the shallow attention network model integrates a coordinate attention(CA)mechanism module and an alpha intersection over union(α-IOU)loss function,and then carries out defect detection on the bolts,steel surfaces,and railings of railway bridges.The test results show that the ACSANet model outperforms the YOLOv5s model using adaptive cropping strategy in terms of the total mAP(an evaluation index)and missing bolt mAP by 5%and 30%,respectively.Also,compared with the YOLOv5s model that adopts the common cropping strategy,the total mAP and missing bolt mAP are improved by 10%and 60%,respectively.Compared with the YOLOv5s model without any cropping strategy,the total mAP and missing bolt mAP are improved by 40%and 67%,respectively.

Improved astrometry of space debris with image restoration

In order to implement an observing strategy, image degradation that occurs during optical observation of space debris is ineluctable and has distinct characteris- tics. Image restoration is presented as a way to remove the influence of degradation in CCD images of space debris, based on assumed PSF models with the same F-WHM as images of the object. In the process of image restoration, the maximum entropy method is adopted. The results of reduction using observed raw CCD images indi- cate that the precision in estimating positions of objects is improved and the effects of degradation are reduced. Improving the astrometry of space debris using image restoration is effective and feasible.

Study on the vehicle license plate tilt correction

The shortage of current different approaches of the vehicle license plate(VLP) tilt correction is analyzed in the paper and a new rotary correction method put forward based on the former ways of the VLP tilt correction in the horizontal direction and the vertical direction Owing to the VLP tilt taking place in the vertical direction,the array of the image’s pixels of the same column is broken,and even different rows come into being superposition.The VLP tilt taking place in the horizontal direction,by which the array of the image’s pixels of the same row broken,and so much as different columns come into being superposition.

A new source extraction algorithm for optical space debris observation

Specific challenges arise in the task of real-time automatic data reduction of optical space debris observations. Here we present an automatic technique that optimally detects and measures the sources from images of optical space debris ob- servations. We show that highly reliable and accurate results can be obtained on most images produced by our specific sensors, and due to optimizations, the whole pipeline works fast and efficiently. Tests demonstrate that the technique performs better than SExtractor from the point of view of fast and accurate detection, therefore it is well suited for data reduction of optical space debris observations.

An improved astrometric calibration technique for space debris observation

An optical survey is the main technique for detecting space debris. Due to the specific character- istics of observation, the pointing errors and tracking errors of the telescope as well as image degradation may be significant, which make it difficult for astrometric calibration. Here we present an improved method that corrects the pointing and tracking errors, and measures the image position precisely. The pipeline is tested on a number of CCD images obtained from a 1-m telescope administered by Xinjiang Astronomical Observatory while observing a GPS satellite. The results show that the position measurement error of the background stars is around 0.1 pixel, while the time cost for a single frame is about 7.5 s; hence the relia- bility and accuracy of our method are demonstrated. In addition, our method shows a versatile and feasible way to perform space debris observation utilizing non-dedicated telescopes, which means more sensors could be involved and the ability to perform surveys could be improved.

Analysis of large-scale UAV images using a multi-scale hierarchical representation

Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.

Super-resolution enhancement of UAV images based on fractional calculus and POCS

A super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets(POCS)for unmanned aerial vehicles(UAVs)images.The representative problems of UAV images including motion blur,fisheye effect distortion,overexposed,and so on can be improved by the proposed algorithm.The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS.The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform(SIFT)for matching.The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail.The objective indices and subjective effect are compared between the proposed and other methods.The experimental results indicate that the proposed method outperforms other algorithms in most cases,especially in the structure and detail clarity of the reconstructed images.