Seolhee Kim¹, Sang-Duck Lee^2,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 195-215, 2024, DOI:10.32604/cmc.2024.052070 - 18 July 2024

Abstract Damage to parcels reduces customer satisfaction with delivery services and increases return-logistics costs. This can be prevented by detecting and addressing the damage before the parcels reach the customer. Consequently, various studies have been conducted on deep learning techniques related to the detection of parcel damage. This study proposes a deep learning-based damage detection method for various types of parcels. The method is intended to be part of a parcel information-recognition system that identifies the volume and shipping information of parcels, and determines whether they are damaged; this method is intended for use in the… More >

Qiqiang Chen^{1, *}, Xinxin Gan², Wei Huang¹, Jingjing Feng¹, H. Shim³

CMC-Computers, Materials & Continua, Vol.65, No.3, pp. 2201-2215, 2020, DOI:10.32604/cmc.2020.011191 - 16 September 2020

Abstract Automatic road damage detection using image processing is an important aspect of road maintenance. It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images. In recent years, deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification. In this paper, we propose a new approach to address those challenges. This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid network for combining multiple scales More >