## 4 Proposed Method
### Modified Hourglass Network  
The hourglass network's architecture, which is made up of stacked hourglass modules, is shown in Figure 9. The hourglass network [21] has been used as the foundation network for numerous research initiatives that have subsequently demonstrated their ability to effectively address human pose [49] estimation problems. The encoder-decoder architecture lightens the network and improves performance, as several studies have shown [50; 51]. In our situation, the hourglass network is used since it is a useful method for resolving problems involving estimates as points, where the network can learn more intricate features by piling modules. While a decoder improves the picture resolution and reassembles features, an encoder first extracts features by decreasing the image resolution. To ensure that the decoder can effectively restore features, the encoder function in an hourglass network is connected to it via a skip connection. An hourglass network is constructed such that the output of the previous stack is mirrored in the current stack via the skip connection, and the input from a previous stack is reflected in the current stack. The remaining blocks plus a skip connection between each stack make up an hourglass module. There is an encoder-decoder structure in every module. Heat maps generated from each stack can be used to measure loss, and by allowing the network to adapt to repeated predictions under intermediate supervision, more stable learning can be achieved.  
Figure 8: Traffic sign board detection workflow.  
Figure 9: Two-Stage Hourglass model.  
The modified hourglass module for a single stage is illustrated in Figure 10. In a 2-stage Hourglass network designed to handle an input resolution of (512 \(\times\) 512 \(\times\) 3), the choice of convolution filter sizes and dimensions plays a vital role in balancing the network's ability to extract fine spatial characteristics while preserving computational efficiency. To minimize the spatial dimensions while enhancing the depth of feature maps, (7 \(\times\) 7) filters are applied with a stride of four. These larger filters cover a broader area of the input image, enabling the acquisition of more global features early in the network without undue loss of information. Using a stride of four considerably reduces the computational effort and memory footprint by decreasing the spatial dimensions by a factor of four in each convolutional operation, changing a (512 \(\times\) 512) dimension into a (128 \(\times\) 128) dimension right at the first layer. Then, mid-level characteristics are recovered using a smaller filter of (3 \(\times\) 3). Bottleneck layers are used to process the most abstract representations of the supplied data. Each residual block has two convolutional layers and one skip connection layer during the downsampling and upsampling processes, including the skip connection modules. For the first convolutional operation in the residual block, a stride of size two is performed, reducing the spatial dimension of the feature map. The remaining convolution processes maintain the spatial dimension while using a size one stride. We apply the 3 \(\times\) 3 kernel size to all convolutional processes. The skip connection layer in the residual blocks compares the spatial and channel dimensions of the input feature maps with the convolution layer's output using linear transformation (1 \(\times\) 1) convolution. Feature maps are produced with five times less spatial resolution and a higher number of channels (256, 384, 384, 512). The nearest-neighbor technique is used to upsample feature maps, with two residual blocks added at each iteration. The final feature map output has (128 \(\times\) 128) spatial dimensions and 256 channels. In addition, we employ attention blocks in the encoder and decoder sections, as well as depthwise separable convolution filters inside the residual blocks, to minimize the number of parameters needed for training, as covered in the following sections.