In the production and construction of industry, safety accidents caused by unsafe behaviors of staff often occur. In a complex construction site scene, due to improper operations by personnel, huge safety risks will be buried in the entire production process. The use of deep learning algorithms to replace manual monitoring of site safety regulations is a powerful guarantee for sticking to the line of safety in production. First, the improved YOLO v3 algorithm is used to output the predicted anchor box of the target object, and then pixel feature statistics are performed on the anchor box, and the weight coefficients are respectively multiplied to output the confidence of the standard wearing of the helmet in each predicted anchor box area, according to the empirical threshold determine whether workers meet the standards for wearing helmets. Experimental results show that the helmet wearing detection algorithm based on deep learning in this paper increases the feature map scale, optimizes the prior dimensional algorithm of specific helmet dataset, and improves the loss function, and then combines image processing pixel feature statistics to accurately detect whether the helmet is worn by the standard. The final result is that mAP reaches 93.1% and FPS reaches 55 f/s. In the helmet recognition task, compared to the original YOLO v3 algorithm, mAP is increased by 3.5% and FPS is increased by 3 f/s. It shows that the improved detection algorithm has a better effect on the detection speed and accuracy of the helmet detection task.

Image semantic segmentation has always been a research hotspot in the field of robots. Its purpose is to assign different semantic category labels to objects by segmenting different objects. However, in practical applications, in addition to knowing the semantic category information of objects, robots also need to know the position information of objects to complete more complex visual tasks. Aiming at a complex indoor environment, this study designs an image semantic segmentation network framework of joint target detection. Using the parallel operation of adding semantic segmentation branches to the target detection network, it innovatively implements multi-vision task combining object classification, detection and semantic segmentation. By designing a new loss function, adjusting the training using the idea of transfer learning, and finally verifying it on the self-built indoor scene data set, the experiment proves that the method in this study is feasible and effective, and has good robustness.

The acceleration of economic globalization and integration has led to a dramatic increase in the flow of goods worldwide and changes in the spatial location of logistics facilities. The location of logistics facilities affects not only the cost and efficiency of cargo transportation activities, but also the rational allocation of logistics resources. Recently, the two major perspectives of logistics space research-cluster (the concentration of logistics facilities and functions in geography) and sprawl (movement of facilities from the urban core to peripheral places) have received extensive attention from academia and policy makers. The evolution of logistics space is influenced by land prices, traffic accessibility, market demand, agglomeration advantages and government policies. The purpose of this study is to present a literature review of logistics space, including data sources, research methods as well as research theories, and to study the impact of logistics space from the perspective of sustainable development. The research results provide some reference for logistics space researchers and logistics facility planners, and play a role in formulating new logistics development strategies and promoting the sustainable development of logistics.

Under the background of the prompt development of the global economy and continuous improvement of environmental protection awareness, end-of-life vehicles (ELVs), as an essential part of “urban mineral”, have the substantial economic, resource, and environmental value. The research on reverse logistics of ELVs has developed rapidly, but the existing relevant reviews are based on unique research perspectives and do not fully understand the whole field. This work aims to help comprehend the research status of reverse logistics of ELVs, excavate and understand the critical publications, and reveal the main research topics in the past 20 years. Based on 299 articles published in ISI Web of Science Core Collection (WOSCC) database from 2000 to 2019, this paper uses the methodologies of literature bibliometrics and content analysis, combined with VOS viewer, CiteSpace, and Bibexcel software. Besides, the literature quantity and cited situation, core journals, distribution of countries and regions, institutions, core authors, subject categories, and keywords information are analyzed to determine the primary trends and future research hot spots focus on reverse logistics of ELVs.

In recent years, traffic congestion has become increasingly serious and the urban environment has deteriorated, posing a challenge to the modern sustainable transportation system. Sustainable travel behavior is a solution that many scholars recognize as being an important aspect in the development of socially, environmentally, and economically sustainable communities. Increasing numbers of studies analyzed the travel choice behavior based on Random Regret-Minimization (RRM) model. RRM considers multiple attribute compromises to capture the traveler's choice behavior based on minimizing the perceived regret decision criteria. Travel route choice and travel mode choice are interrelated and mutual restraint when a traveler makes a travel decision. To our knowledge, there are limited literatures that overall considered travel mode and travel route choice behavior based on RRM at present. This paper aims to fill this gap and presents a literature review for the application of RRM on sustainable travel mode and travel route choice behavior from empirical issues, influencing factors, theories and methods to evaluate RRM's potential and limitations as a discrete model of travel choice behavior. The results will provide reference for researchers to study this field and develop novel strategies to promote the sustainable traffic system in the future.