## 5 Experiments and Results
### Qualitative Analysis  
#### 5.2.1 Heatmap Regression and Bounding Box Predictions  
Figure 17 illustrates the predictions of the modified two-stage hourglass model. The object center points are decoded from each class of confidence maps by performing the argmax and using a threshold value rather than non-maximum suppression for faster inference. Now that the center coordinate values are available for the downsampled output, the upsampled center coordinates are found by subtracting the offset error given by the offset prediction array. Similarly, the width and height of the objects are decoded from the width-height prediction array. From the depth map, the approximate distance of the sign boards is calculated. Figure 18 illustrates some of the bounding box predictions of easy samples decoded from the prediction heads of the modified two-stage hourglass model. Figure 18a-d shows various attribute class bounding boxes located within the general information sign board class. Figure 18e-h shows the attribute class bounding boxes for national highway, prefectural, regulatory, and warning sign board classes. Figure 17: _Cont._  
Figure 19 illustrates some of the challenges faced during the bounding box predictions of hard samples. Figure 19b-d,g show some cases where English alphabets and numbers are missed because of very small object sizes and tilted sign boards. Figure 19a,e-h show some cases where the text or arrow attribute appears in the exact center of the outer object category. The object prediction heads can only hold one value per pixel, and as a result, the model learns either the text size or the larger object size. This issue can be resolved using extra prediction head channels for holding the main object (outer) and the attributes (inner), which increase the model parameters, or by using sliding-window techniques. The issue of objects within objects is not addressed in this paper because of the performance issues caused by the need for extra prediction heads. However, in the case of text attributes that were missed in between the words, the problem is resolved during the post-processing stage when converting the characters into words using morphology, dilation, and contour detection. We set the dilation filter size to match the word length required for detection. Figure 17: Prediction results of the proposed method for the synthetic dataset. (**a**) Input image; (**b**) Width prediction; (**c**) Height prediction; (**d**) Offset x prediction; (**e**) Offset y prediction; (**f**) Heatmap prediction; (**g**) Depth map prediction; (**h**) Bounding Box prediction. Figure 18: Bounding box predictions for easy samples (**a–h**). Figure 20 illustrates the depth map predictions for some of the test images. We set the maximum distances up to 100 m while creating the dataset, and hence the model can learn to estimate depths up to 100 m. Nearer objects have brighter pixels, and farther objects have darker pixels. The depthmap pixel values are denormalized to find the estimated distance of the object. Figure 19: Bounding box predictions for hard samples (**a**-**h**). Figure 20: Depth map predictions for validation samples (**a**-**h**). Figure 21 illustrates the bounding box predictions for some of the real images and video frames from the in-vehicle camera. There are some missed predictions for smaller text characters. Figure 21a-d shows the predictions for real images; Figure 21e-h shows the predictions for video frames; and Figure 21i-l shows the missed predictions of texts when the signboard is farther, smaller, or of poor quality. The outermost bounding box class predictions seem to be stable for almost all the cases in the real-world dataset. There is definitely room for improvement in the model to detect objects that are smaller. Figure 22 illustrates the encoder stage's convolution filter outputs with and without attention blocks for the modified hourglass model. It is visible how the current spatial and channel attention mechanisms help the model learn better features.