# 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
#### 5.2.3 Ablation Experiment  
In order to assess the necessity and effectiveness of each improvement module, we conducted ablation experiments to evaluate their impact on different evaluation metrics. This allowed us to determine the specific role of each module within the algorithm. The comparative results of the ablation experiments can be found in Table 6.  
After replacing the backbone network with FasterNet, the precision was observed to decrease by 2.5%, indicating a comparable performance to the original network, the AP\({}_{50}\) metric showed a decline of 0.7%, while the FPS improved by 56.27 f/s. Furthermore, computational complexity dropped by 6.7 GFLOPs, along with a 2.51 M reduction in parameter count. The number of parameters decreased the most, mainly due to the effect of PConv convolution and its variants, which enable the model to extract features through partial convolution. These results suggest that the network demonstrates the property of being lightweight on the given datasets.  
To compensate for the accuracy drop caused by parameter reduction, the introduction of a ternary attention mechanism balances this issue. It improves accuracy by 1.0% without significantly increasing the number of parameters, as it optimizes feature weight allocation through cross-attention across different dimensions (H, W, C), rather than relying on a large stack of convolutional kernels. By introducing attention mechanisms, the focus of the network on hooks has notably heightened, enhancing its ability to capture global information, which keeps AP\({}_{50}\) roughly stable. Additionally, detection speed increases by 97.06 f/s, while computational complexity and parameter size remain consistent with the original algorithm. Therefore, the ternary attention mechanism is almost a parameter-free attention mechanism to balance the accuracy loss caused by using FasterNet with parameters.  
In general, by employing Slim-neck to replace the neck structure, an enhancement of 0.2% in precision is observed, while AP\({}_{50}\) is consistent with the indicators of the benchmark model. Additionally, the group convolution and depth separable convolution operations of GSConv in Slim-neck effectively reduce computational complexity and workload, accelerating image inference speed, detection speed increases by 41.74 f/s, accompanied by a decrease of 33 GFLOPs in computation and a decrease in parameter count by 0.86 M; these improvements across various evaluation metrics highlight the effectiveness of GSConv convolution.  
Furthermore, the Dyhead of the head module is not optimal in terms of model complexity and processing speed, but its introduction is mainly to balance the lightweight effect of Faster Net. When the head was replaced solely with Dyhead, there was a substantial increase of 0.4% in precision. However, the AP\({}_{50}\) exhibited a slight decline of 1.4%, which remained within an acceptable range. The FPS experienced a notable improve  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Faster Net** & **Triplet Attention** & **Slim-Neck** & **Dyhead** & **P** & **AP\({}_{50}\)** & **FPS** & **FLOPs/G** & **Parameters/M** \\ \hline - & - & - & - & 0.973 & 0.977 & 95.238 & 28.4 & 11.125 \\ \(\surd\) & - & - & - & 0.948 & 0.970 & 151.515 & 21.7 & 8.616 \\ - & \(\surd\) & - & - & 0.983 & 0  
ment of 29.76 f/s, while the computational complexity and parameter count reduced by 0.3 GFLOPs and 0.275 M, respectively. These findings collectively demonstrate the value of each improvement introduced in the algorithm.