# File-YOLO: A Faster, Lighter, and More Efficient Strategy for Autonomous Tower Crane Hook Detection
## 5 Experimental Results and Discussion
### Comparative Analysis of Experimental Results
#### 5.2.2 Performance Comparison of Different Networks  
In order to evaluate the performance of the improved models in terms of detection accuracy, detection speed, and lightweight design, this study selects five metrics: P (Precision), AP50 (Average Precision at 50% IoU), FPS (Frames Per Second), FLOPs (Floating Point Operations per Second), and Parameters. These metrics serve as the standards for assessment. To assess the generalizability of the model, the first experiments using two extensively utilized large public datasets: COCO2017, which consists of 80 categories, and VOC2012, which consists of 20 categories. The performances of the FLE-YOLO and YOLOv8 networks on these datasets were evaluated using five metrics, as shown in Table 4.  
During the model validation process, the batch size was set to 32 uniformly, and the weight outcomes of different network trainings were assessed. The results from the experiments revealed that FLE-YOLO performs optimally in terms of precision and mAP50. FLOPs, and parameters on the VOC2012 dataset, with precision increased by 2% and 1%; \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Category** & **Model** & **P** & **mAP50** & **FPS** & **FLOPs/G** & **Parameters/M** \\ \hline \multirow{4}{*}{VOC2012} & 20 & YOLOv8 & 0.781 & 0.741 & 111.11 & 28.5 & 11.133 \\  & 20 & YOLOv12s & 0.791 & 0.756 & **208.33** & 21.3 & 9.238 \\  & 20 & FLE-YOLO & **0.801** & **0.789** & 114.942 & **19.4** & **8.889** \\ \cline{1-1}  & 80 & YOLOv8 & 0.663 & 0.5776 & 102.618 & 28.6 & 11.157 \\ \cline{1-1}  & 80 & YOLOv12s & **0.689** & **0.606** & **208.33** & 24.1 & 9.261 \\ \cline{1-1}  & 80 & FLE-YOLO & 0.672 & 0.5777 & 106.382 & **19.6** & **8.809** \\ \hline \hline \end{tabular} Note: the bold data represent the optimal values.  
\end{table} 
Table 4: Comparison of performance between FLE-YOLO and YOLOv8 on public datasets. Figure 12: Comparison of heat map results. (**a**) Original Image; (**b**) YOLOv8 heatmap; (**c**) FLE-YOLO Heatmap. mAP\({}_{50}\) increased by 4.8% and 3.3%. In inference speed, FPS is the highest for YOLOv12s, while FLE-YOLO increases by 3.832 f/s compared to YOLOv8s. And the computational complexity and parameters decreased by 9.1 G, 1.9 G and 2.244 M, 0.349 M, respectively. On the COCO2017 dataset, compared to the original algorithm, the FLE-YOLO maintained similar levels of precision and mAP\({}_{50}\), with an increase in detection speed of 3.764 f/s but slightly less than YOLOv12s. In terms of computational cost, FLE-YOLO performs better, with the computational complexity and parameters reduced by 9 G, 4.5 G and 2.248 M, 0.452 M, respectively. From the experimental results, as the number of categories increases, the performance of precision and mAP\({}_{50}\) shows a slight downward trend, but it can still maintain a basically consistent detection effect with the original algorithm. The occurrence of this phenomenon may be due to the fact that as the number of target categories increases, the feature space becomes more complex, requiring more complex models to capture more category features. It is worth noting that the improved algorithm has outstanding advantages in its lightweight design compared to the original algorithm. Therefore, the improved algorithm may exhibit better performance for various evaluation indicators on single or few category datasets. As shown in Table 5, the performances of various networks on the crane hook datasets developed for this research were evaluated. The experimental results indicate that the FLE-YOLO basically outperforms other single-stage models. Compared with YOLOv8, it maintains the same precision while improving all other indicators. The mAP\({}_{50}\) has increased by 0.6%, with detection speed increased 47.619 f/s. The computational complexity and paramaters have decreased by 9 GFLOPs and 3.537 M, respectively. This indicates that the FLE-YOLO algorithm in this article effectively inherits the functions of various improved modules on the original YOLOv8s algorithm.