## 5 Results and Discussion  
Here we conduct comprehensive ablation studies to uncover Res-CNN3's potential in wind turbine blades defects detection from RGB images. These ablations are based on the residual network's object detection performances on the entire wind farm's wind turbine blade defects dataset. The backbones in each ablation experiment are the individual Resnet models with different numbers of parameters, and an improvement based on the TCCS approach. We ablate the backbones (i.e., ResNet-34 [17], ResNet-101 [17] etc.) of Res-CNN3 to observe the object detection performances in Precision, Recall, mAP50 (%) and Average test time (s). All the following ablation study experiments are conducted on the complete training dataset. The experimental results in Table 3 show that the Res-CNN3 wind turbine blades defect detection model has a mAP of 80.6%, a parameter size of 51.7 M, and an average test time of 0.036 s. Compared with ResNet-50 + TCCS + SS, the number of parameters is increased by 26.1 M, the average test time of each image decreases by 0.006 s, and the precision decreases by 0.7%. This evidently shows that the addition of TCCS + SS to ResNet-50 certainly improves in detection accuracy performance, but slightly degrades in detection test time, which is a unique scenario that calls for a selection of a technique based on a compromise in one aspect of the technique's performance. Each simplification strategy tweaks the configuration of channels and the number of parameters on the basis of the original and can also ensure that the loss of accuracy is not large. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Complete 700 Epochs** \\ \hline Batch size & 16 \\ \hline Learning rate & 0.03 \\ \hline Momentum & 0.88 \\ \hline Weight decay & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental parameter settings. In Figure 4 below, it appears that most of the methods, on average, perform terribly in the categories of Fatigue damage and Leading Edge erosion. This can be explained by the fact that these categories of defects involve small object detection. The topic of small object detection is usually controversial and challenging. The primary issue is that fine target defects have a limited number of pixels, making it challenging to extract useful feature information [15; 28]. Figure 4 displays the outcomes of various algorithms on the dataset for defective wind turbine blades. The target detection algorithms all deliver satisfactory results for large target defects. The detection performance of small targets, however, still has a significant gap when compared to that of large targets. The majority of algorithms seriously miss small target defects in the second and third rows of images. It is challenging to extract the useful features of small target defects because they have few features. In terms of Structural cracks, Leading Edge erosion, and Delamination defects detection, YOLO performs remarkably well. It also achieves good detection performance for both big and small target defects. Following YOLO, the most promising technique is the ResNet-50 + TCCS + SS, which performs exceptionally well on Structural cracks, Leading Edge erosion and Delamination defects detection in a like manner, but with a lower confidence score, compared to YOLO. Moreover, ResNet-50 + TCCS + SS outperforms Res-CNN3 in Structural cracks, Leading Edge erosion and Delamination defects detection accuracy, but not in Fatigue damage detection accuracy. However, Res-CNN3 still outperforms Res  
Figure 4: Average Precision (AP) of the proposed model compared to other state-of-the-art models for detection of different wind turbine blade defects.