## 5 Results
### Study Results  
Comparison study results were determined as:  
* Training time costing: The time costing of 100 epochs of each model are shown in Figure 6.  
It can be seen that among 12 models, the VGG16-BF was the fastest (113 s) and the Xception was the slowest (3604 s). * Accuracy change during training: Generally, in the training process of deep learning models, with the constant updating of neuron parameters, the accuracy of the model in truck classification is elevated. To reflect the accuracy changes during training, two accuracy curves based on the training set itself and the validation set, respectively, were adopted for each model, and by analyzing the two accuracy curves, the model training performance can be evaluated. Generally, models  
Figure 6: Training time costing results. with a smaller curve fluctuation during accuracy elevation have better training convergence, and models with two closer accuracy curves of the training set and the validation set and higher accuracy have a better training performance. The accuracy curves of each model in the training process are shown in Figure 7. Here, the front mark of NTL refers to not adopting TL, while BF or FT refers to the corresponding TL; the rear mark of _Train refers to the accuracy curve basing on the training set, while _Validation refers to the accuracy curve based on the validation set. It can be seen that, among the 12 models, the VGG16-FT in **(a)** had the best convergence and training performance. For VGG16-FT, both its accuracy curves based on the training set and validation set had small fluctuation (only about 2% after the 20 th epoch), thus its convergence can be considered as good. Meanwhile, its two accuracy curves were the closest by contrast, and at the later stage of training, its accuracy based on the training set approached 100% while its accuracy based on the validation set approached 98% (the highest among the 12 models), thus its training performance can also be considered as the best. Other relatively good deep learning models include the VGG16-BF, the InceptionV3-BF, and the Xception-BF in **(a)**, **(b)**, and **(c)**. The VGG16, in **(a)**, was the worst in training outcomes compared to the other models, as both its accuracy curves had a low accuracy of 68% before the 78 th epoch, and did not break through the 90% accuracy at the end of the training. The InceptionV3-FT, in **(b)**, and the Resnet50-FT, in **(d)**, both had poor training convergence, as their accuracy curves based on the validation set had large fluctuation (over 20% and 40%, respectively), and the differences between their two curves were also large. * Testing results of trained models: As deep learning models are trained, the testing set can be used to test the models' usability and working performance. Here, testing accuracy and speed  
Figure 7: Training accuracy changing of deep learning model prototypes and TL forms: **(a)** VGG16 and VGG16-TL; **(b)** InceptionV3 and InceptionV3-TL; **(c)** Xception and Xception-TL; **(d)** Resnet50 and Resnet50-TL. are the main indicators for evaluating a model, and to reach a satisfactory application, the truck classification accuracy should be over 95% to be seen as qualified, and the working speed should be as fast as possible. The testing accuracy and testing speed results are shown in Figures 8 and 9. It can be seen that, among 12 models, the VGG16-FT had the highest accuracy of full/empty-load truck classification (98%), and its test speed was also the fastest (41.1 images/s). VGG16-BF, InceptionV3-BF, and Xception-BF all reached the qualified level of testing accuracy (over 95%) but had slower testing speeds than the VGG16-FT. The Xception had the lowest testing accuracy of the full/empty-load truck classification (40.6%). The InceptionV3-BF had the slowest testing speed (1.1 images/s).