Contrastive Study on the Automatic Identification of Urban Construction Waste in High-Resolution Remote Sensing Images
The construction of datasets is the most fundamental task in deep learning target recognition,which largely determines the accuracy of target recognition.To study the impact of different annotation forms of datasets on the recognition and detection performance of construction waste,three types of annotation boxes,namely regular rectangle,rotating rectangle,and polygon,were used to annotate construction waste in high-resolution remote sensing images.The regular rectangle construction waste dataset,rotating rectangle construction waste dataset,and polygonal construction waste dataset were constructed.Comparative analysis of experimental results reveals that the final recognition accuracy and precision of the polygonal annotation box form are the highest,making it the most suitable for constructing a construction waste recognition model.Both rectangular and polygonal construction waste recognition models can effectively achieve automatic positioning,recognition,and extraction of construction waste.The polygonal construction waste recognition model can also recognize the contour of construction waste,estimate the volume of construction waste based on the height of on-site construction waste,and provide data foundation and technical support for precise control of construction waste.
construction wastehigh-resolution remote sensing imagetarget detectiondeep learning
NETL
NSTL
万方数据
