Despite various economic crisis situations around the world, the courier and delivery service market continues to be revitalized. The parcel shipping volume in Korea is currently 3.37 billion parcels, achieving a growth rate of about 140% compared to 2012, and 70% of parcels are from metropolitan areas. Given the above statistics, this paper focused on the development of an underground logistics system (ULS), in order to conduct a study to handle the freight volume in a more eco-friendly manner in the center of metropolitan areas. In this paper we first analyzed the points at which parcel boxes were damaged, based on a ULS. After collecting image data of the parcel boxes, the damaged parcel boxes were detected and classified using computerized methods, in particular, a convolutional neural network (CNN), MobileNet. For image classification, Google Colaboratory notebook was used and 4882 images were collected for the experiment. Based on the collected dataset, when conducting the experiment, the accuracy, recall, and specificity of classification for the testing set were 84.6%, 82% and 88.54%, respectively,. To validate the usefulness of the MobileNet algorithm, additional experiments were performed under the same conditions using other algorithms, VGG16 and ResNet50. The results show that MobileNet is superior to other image classification models when comparing test time. Thus, in the future, MobileNet has the potential to be used for identifying damaged boxes, and could be used to ensure the reliability and safety of parcel boxes based on a ULS.

Parcel storage provides last-mile delivery services as part of the logistics process. In order to build an intelligent system for parcel storage, we conducted a study on parcel box recognition using AI’s deep learning technology. Box detection and location estimation studies were conducted using the YOLOv5 model for parcel recognition, and this model is presently being applied to many studies because it has excellent object recognition and is faster than previous models. The YOLOv5 model is classified into small, medium, large, and xlarge according to the size and performance of the model. In this study, these four models were compared and analyzed to perform an experiment showing the optimal parcel box recognition performance. As a result of the experiment, it was determined that the precision, recall, and F1 of the YOLOv5large model were 0.966, 0.899, and 0.932, respectively, showing a stronger performance than the other models. Additionally, the size of the YOLOv5large is half that of YOLOv5xlarge, and the YOLOv5large showed the optimal performance in object recognition of the parcel box. Therefore, it seems that the basis for building an intelligent parcel storage system, which shows optimal efficiency in real time using the YOLOv5large model, can be laid through the parcel object recognition experiment conducted in this study.

Today, a lot of research on autonomous driving technology is being conducted, and various vehicles with autonomous driving functions, such as ACC (adaptive cruise control) are being released. The autonomous vehicle recognizes obstacles ahead by the fusion of data from various sensors, such as lidar and radar sensors, including camera sensors. As the number of vehicles equipped with such autonomous driving functions increases, securing safety and reliability is a big issue. Recently, Mobileye proposed the RSS (responsibility-sensitive safety) model, which is a white box mathematical model, to secure the safety of autonomous vehicles and clarify responsibility in the case of an accident. In this paper, a method of applying the RSS model to a variable focus function camera that can cover the recognition range of a lidar sensor and a radar sensor with a single camera sensor is considered. The variables of the RSS model suitable for the variable focus function camera were defined, the variable values were determined, and the safe distances for each velocity were derived by applying the determined variable values. In addition, as a result of considering the time required to obtain the data, and the time required to change the focal length of the camera, it was confirmed that the response time obtained using the derived safe distance was a valid result.

With the recent increase in the utilization of logistics and courier services, it is time for research on logistics systems fused with the fourth industry sector. Algorithm studies related to object recognition have been actively conducted in convergence with the emerging artificial intelligence field, but so far, algorithms suitable for automatic unloading devices that need to identify a number of unstructured cargoes require further development. In this study, the object recognition algorithm of the automatic loading device for cargo was selected as the subject of the study, and a cargo object recognition algorithm applicable to the automatic loading device is proposed to improve the amorphous cargo identification performance. The fuzzy convergence algorithm is an algorithm that applies Fuzzy C Means to existing algorithm forms that fuse YOLO(You Only Look Once) and Mask R-CNN(Regions with Convolutional Neuron Networks). Experiments conducted using the fuzzy convergence algorithm showed an average of 33 FPS(Frames Per Second) and a recognition rate of 95%. In addition, there were significant improvements in the range of actual box recognition. The results of this study can contribute to improving the performance of identifying amorphous cargoes in automatic loading devices.

The automobile industry has developed dramatically in recent years, the supply of vehicles has also increased, and thus it has become deeply established in everyday life. Recently, as the supply of vehicles with autonomous driving functions increases, the safety of vehicles is also an emerging issue. Various car-following models for the safe driving of vehicles have long been studied by various people, and recently, a Responsibility-Sensitive Safety (RSS) model has been proposed by Mobileye. However, in existing car-following models or the RSS model, the safe distance between vehicles is presented using only vehicle speed and acceleration information, so there is a limitation in that it cannot respond to changes in road conditions due to the weather. In this paper, in order to ensure safety when the RSS model is applied to a variable focus function camera, an improved RSS model is presented in consideration of the changes in road conditions due to changes in weather, and a safety distance is derived based on the proposed model.