## 7 Conclusions  
This paper presented the framework of a novel, automated field earthmoving quantity statistics (FEQS) that mainly applies vision-based deep learning for full/empty-load truck classification as the core work and counts full-load trucks (Figure 2). The proposed FEQS contributes to relieving current problems in FEQS of manual, laborious, high cost, truck traffic interference, continuous maintenance demands, and application limitations, because it utilizes the field-equipped surveillance video system and deep learning CNN-related image recognition models to achieve unmanned and non-contact truck load condition judgement. As deep learning CNN-related models (prototypes and TL forms) are numerous, the authors introduced a comparison study to test and evaluate CNN-related models' performance in full/empty-load earthmoving truck classification. Thus, the core work of the proposed FEQS framework can be assessed in terms of feasibility, and well-performed models can be identified for model choice suggestions in future FEQS implementation. The comparison study involved 12 deep learning models constructed by four classical CNNs of VGG16, InceptionV3, Xception, and Resnet50 and two popular TL methods of BF and FT. Based on a proper earthmoving project scenario, the training and testing results of the 12 models were obtained. The study results showed that, on the whole, the VGG16-FT was the optimal model among the 12 models, as it had the highest working accuracy of 98% and the fastest truck image classification speed of 41.1 images/s. In addition, the VGG16-BF, InceptionV3-BF, and Xception-BF all reached the satisfactory goal of a working accuracy of over 95%, and showed advantages in model training, but their working speeds were not as fast. Hence, the VGG16-FT was able to achieve full/empty truck classification in the application level, and other three CNN-BF models also had further application potential. It can be concluded that, through the comparison study, the core work of the proposed vision-based FEQS framework was proven as theoretically feasible. Further discussion showed that compared to CNN prototypes, their TL forms generally have a better working accuracy and training performance in full/empty truck classification, and the four classical CNN prototypes all have relatively lower working accuracy than their TL forms. For CNN-TL models, generally, CNN-BF models have the advantage in working accuracy, while CNN-FT models have the advantage in working speed. Hence, in industrial applications, TL forms of CNN are recommended to replace their prototypes, and the specific choice of CNN-TL models depends on the working demand. Model choice suggestions are CNN-BF models are more suitable for tasks with a high accuracy demand, while CNN-FT models are more suitable for real-time tasks. This paper provides a reference and support for the application of vision-based deep learning CNN and CNN-TL models in earthmoving operations, civil engineering management, and intelligent engineering. Limitations about the current study lie on the fact that the proposed vision-based FEQS requires quarantined projects in an open field that use uniform and uncovered trucks. However, the requirement for non-occluded scenes is a common limitation of vision-based methods, and the other constraints are fulfilled in a large number of infrastructure projects as indicated in the literature review. Nevertheless, future work will involve applying vehicle recognition before performing full/empty classification to exclude unwanted vehicles and to extract truck information in advance. Moreover, using machine vision to recognize the load weight of covered trucks will also be studied in the future to replace current weighing methods with a non-contact method. Conceptualization, Q.L., Z.S. and C.F. ; methodology, Q.L., C.F. and Z.S. ; software, C.F. ; validation, Q.L., Z.S. and J.L. ; formal analysis, Q.L. and J.L. ; resources, J.Z. ; Writing--Original draft preparation, Z.S. and C.F. ; Writing--Review and editing, Q.L. and J.L.