## 1 Introduction
Nevertheless, these methods require postprocessing and non-maximum suppression, and they present difficulties when used with custom datasets, especially in situations where precise text character extraction and correct depth estimation are crucial. In recent years, numerous techniques for anchor-free detection have been developed. Gaussian heatmap regression is used to identify the object keypoints, which helps these methods overcome the issue of class imbalance in ROI proposals and the slower anchor-box design option. Pairs of a bounding box's corners are used by CornerNet [10; 11] to identify items. By locating one center point and four extreme points spaced out in various directions, ExtremeNet [12] is able to recognize objects. CenterNet [13] recognizes objects as points based on their center x-y coordinates. Compared to anchor-based detection techniques, these anchor-free techniques can yield higher performance. In the early stages of traffic signboard object detection, color, shape, and machine learning methods employing Support Vector Machines (SVM) and Histograms of Oriented Gradients (HOG) features [14] for widely used datasets were the main methods used. Color-shape-based detections are easier to use and quicker but because they depend on precise processing and are unstable in the face of changing lighting and environmental factors, they are not very accurate. Because Hue, Saturation, Value (HVV) color space is resilient to circumstances like occlusion and is invariant to changes in illumination, it performs segmentation better than other color spaces [15]. Certain machine learning techniques still have trouble finding a decent balance between processing time and accuracy when working with small traffic signs and high-resolution photos. On the German Traffic Sign Detection Benchmark (GTSDB) dataset, improved HSV and HOG algorithms were obtained with good detection rates [16]. Other items, such as traffic signs, were detected using the Haar-like cascade characteristics [17]. The Haar-like features method outperforms the HOG features method in terms of speed and usefulness when it comes to identifying fading and blurry traffic signs [18]. Furthermore, SVM-based techniques frequently require the extraction step in order to obtain the region of interest, which has a significant impact on the SVM-based detectors' performance. In terms of accuracy and speed, CNN-based detection networks perform far better than the aforementioned methods. The German Traffic Sign Recognition Benchmark (GTSRB) dataset utilizes You Only Look Once (YOLOv3 to identify the traffic sign boards [19]. In [20], YOLOv4 with an attention mechanism was created. Although these algorithms have demonstrated good performance, the detection accuracy is still not at a reasonable level because of the small size of traffic signs, imaging angles, and complex illumination environments found in real-world scenarios. In this paper, we propose a custom anchorless object detection model designed specifically for 3D traffic sign boards with enhanced depth estimation and text character detection. Our model uses a modified stacked hourglass network [21] to extract features from the input image, as well as spatial and channel attention at encoder stages. In our previous conference paper [22], we performed experiments on spatial and channel attention modules for encoder-decoder networks and found the best position to place the attention modules. Also, we use the depthwise and pointwise convolution layers at the bottleneck layers to reduce the model parameters. For the custom dataset, we compare our method with the YOLOv3 model in terms of accuracy and speed. It was found that our model can better detect smaller text characters compared to the YOLOv3 model, which is crucial in extracting meaningful information from the traffic signs.