# EGCY-Net: An ELAN and GhostConv-Based YOLO Network for Stacked Packages in Logistic Systems
## 3 Results
### Box Type and Quantity
#### 3.2.2 Experiment Results  
To evaluate whether the proposed method has an advantage over state-of-the-art studies, several remarkable CNNs for object detection were chosen for comparison, namely, YOLOv3 [23], YOLOv5l [24], YOLOR [25], and YOLOv7 [14]. All the comparative methods were reproduced using the default settings given by the authors, and all models were trained for 20 epochs only. The overall results are in Table 3. After all models were trained for 20 epochs, we chose the best model to be trained with more epochs for implementation in Jetson Nano. The following conclusions can be drawn from Table 3:  
1. The parameter count of the EGCY-Net is approximately 30 M, while the size of the ONNX model is 111 MB. The result was the lowest compared to the other SOTA models in the experiment. In addition, the EGCY-Net testing time was 5.96 seconds, making it the fastest compared to the others. 2. EGCY-Net achieved mAP scores of 87.1%, 86.5%, 87.3%, 86.6%, and 88.5% for all, 04, 13, A, and Y classes, respectively, when evaluated at the IoU threshold range of 0.5 to 0.95. 3. The proposed network in YOLOv7x [14] achieved good results. Nevertheless, this approach had the highest number of parameters and the largest ONNX model size compared to the other algorithms. 4. YOLOv3 [23] had greater precision compared to YOLOv5l [24], YOLOv7 [14], and YOLOR [25], although it had more parameters and a larger model size. The YOLOv7 [14] also performed better when the size of the parameters was small. Additionally, YOLOv5l [24] had the third-fewest parameters, behind YOLOv7 [14] and EGCY-Net, but its performance was inferior to the other two models. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Model** & **YOLOv3**[23] & **YOLOv5l**[24] & **YOLOv25** & **YOLOv7x**[14] & **YOLOv7**[14] &
\begin{tabular}{c} **EGCY-Net** \\ **(Ours)** \\ \end{tabular} \\ \hline \multirow{4}{*}{All} & P (\%) & 99.4 & 98.9 & 99.3 & 99.5 & 99.6 & 99.6 \\  & R(\%) & 99 & 98.9 & 99.1 & 99.6 & 99.7 & 99.9 \\  & [EMAIL_ADDRESS](\%) & 99.4 & 99.5 & 99.5 & 99.5 & 99.5 & 99.5 \\  & [EMAIL_ADDRESS](\%) & 84.8 & 83.7 & 84 & 86.3 & 84.4 & **87.1** \\ \hline \multirow{4}{*}{04} & P (\%) & 99.6 & 98.7 & 99 & 99.7 & 99.5 & 99.8 \\  & R(\%) & 99 & 98.8 & 98.7 & 99.6 & 99.2 & 99.5 \\  & [EMAIL_ADDRESS](\%) & 99.5 & 99.5 & 99.5 & 99.6 & 99.5 & 99.5 \\  & [EMAIL_ADDRESS](\%) & 84.6 & 83 & 83.6 & 85 & 82 & **86.5** \\ \hline \multirow{4}{*}{13} & P (\%) & 99.3 & 99.1 & 98.8 & 99.3 & 99.3 & 99.1 \\  & R(\%) & 99.7 & 99.5 & 99.1 & 99.8 & 99.7 & 100 \\  & [EMAIL_ADDRESS](\%) & 99.5 & 99.5 & 99.5 & 99.5 & 99.5 & 99.5 \\  & [EMAIL_ADDRESS](\%) & 84.1 & 84.1 & 84.5 & 85.9 & 85.1 & **87.3** \\ \hline \multirow{4}{*}{A} & P (\%) & 99.3 & 99.5 & 99.8 & 99.7 & 99.8 & 99.8 \\  & R(\%) & 97.5 & 98.1 & 98.7 & 99.6 & 100 & 100 \\  & [EMAIL_ADDRESS](\%) & 99.4 & 99.4 & 99.5 & 99.4 & 99.5 & 99.5 \\  & [EMAIL_ADDRESS](\%) & 85.9 & 84.1 & 84.9 & **86.6** & 84 & **86.6** \\ \hline \multirow{4}{*}{Y} & P (\%) & 99.4 & 98.4 & 99.7 & 99.2 & 99.7 & 99.7 \\  & R(\%) & 100 & 99.3 & 99.9 & 99.6 & 99.9 & 100 \\ \cline{1-1}  & [EMAIL_ADDRESS](\%) & 99.4 & 99.5 & 99.4 & 99.5 & 99.5 & 99.5 \\ \cline{1-1}  & [EMAIL_ADDRESS](\%) & 84.5 & 83.8 & 83.1 & 87.6 & 86.5 & **88.6** \\ \hline \multicolumn{2}{c}{Parameter (M)} & 61.5 & 46.1 & 52.4 & 70.8 & 36.4 & **30** \\ \hline \multicolumn{2}{c}{ONNX model Size (MB)} & 234 & 176 & 200 & 270 & 139 & **111** \\ \hline \multicolumn{2}{c}{Inference time (s)} & 13.6 & 17.32 & 14.829 & 12.995 & 12.744 & **5.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results between EGCY-Net and SOTA models. To demonstrate the efficacy of the proposed algorithm, images from the test subset were selected at random for comparison. The results from tests conducted using smartphone images are presented in Figure 15.  
In addition, to evaluate the efficacy of the enhanced algorithm proposed in this paper, we chose a test image captured by the Arducam, which has a lens that is distinct from the smartphone camera.