###### Abstract  
Various factors such as natural disasters, vandalism, weather, and environmental conditions can affect the physical state of traffic signs. The proposed model aims to improve detection of traffic signs affected by partial occlusion as a result of overgrown vegetation, displaced signs (those knocked down, bent), perforated signs (those damaged with holes), faded signs (color degradation), rusted signs (corroded surface), and de-faced signs (placing graffiti, etc., by vandals). This research aims to improve the detection of bad traffic signs using three approaches. In the first approach, Spiral Pooling Pyramid-Fast (SPPF) and C3 TR modules are introduced to the architecture of Yolov5 models. SPPF helps provide a multi-scale representation of the input feature map by pooling at different scales, which is useful in improving the quality of feature maps and detecting bad traffic signs of various sizes and perspectives. The C3 TR module uses convolutional layers to enhance local feature extraction and transformers to boost understanding of the global context. Secondly, we use predictions of Yolov5 as base models to implement a mean ensemble to improve performance. Thirdly, test time augmentation (TTA) is applied at test time by using scaling and flipping to improve accuracy. Some signs are generated using stable diffusion techniques to augment certain classes. We test the proposed models on the CCTSDB2021, TT100K, GTSDB, and GTSRD datasets to ensure generalization and use k-fold cross-validation to further evaluate the performance of the models. The proposed models outperform other state-of-the-art models in comparison. b +
Footnote †: journal: Medical  
by physical, weather, and lighting conditions. These techniques include modification to deep learning architecture [8; 9], ensemble methods in Machine and Deep [10], and test time augmentation [11], among others. To address the low detection accuracy of obscure traffic signs, [PERSON] et al. [12] proposed a method for detecting and recognizing partially obscured traffic signs during vehicle motion. The authors use a color-shape recognition approach to extract traffic sign regions and fuse information from multiple frames to enhance the accuracy of the traffic sign information. The algorithm can identify traffic signs not recognized by YOLOV4 and YOLOV8 when a vehicle is moving at speeds of 18 km/h, 36 km/h, and 54 km/h. [PERSON] et al. [13] introduced an adaptive enhancement technique to improve the quality of images in complex illumination scenes. The authors integrate a lightweight attention block into a single-stage target detection algorithm SSD with ResNet and VGG as the backbone networks. Their approach is effective in detecting and recognizing images in dark scenes. [PERSON] et al. [14] used predictions from ResNet50, DenseNet121, and VGG16 pre-trained models to enhance traffic sign detection by implementing a voting ensemble. They evaluated their method on the German Traffic Sign Recognition Benchmark (GTSRB), the Belgium Traffic Sign Dataset (BTSD), and the Chinese Traffic Sign Database (TSRD) datasets, achieving accuracies of 98.84%, 98.33%, and 94.55%, respectively. [PERSON] et al. [15] proposed a Context-Aware module and an Attention-Driven Weighted Fusion Network module for the detection of small and occluded traffic signs. The Context modules help the model to understand the broader context in which traffic signs exist, which is useful in detecting small and occluded traffic signs. The results of the proposed method show improved performance on the TT100K traffic sign. In this study, we use Yolov5 models to improve the detection accuracy of bad traffic signs because of their speed, support for real-time detection, range of sizes suitable for our model, ease of customization, and support for ensemble and test time augmentation (TTA) techniques.