Abstract
Aims: A Robust Approach to Object Detection in Images using Improved T_CenterNet Method
Background: Currently, two-stage image object detectors are the most common method of detecting objects. However, due to the slow processing of two-stage detectors, it is not always possible to apply them in real-time.
Objective: To develop a more robust and effective method for detecting objects in images by building upon and improving the T_CenterNet algorithm
Method: In this research paper, we propose CenterNet, a method based on a one-stage detector that takes into consideration the heat map branch of CenterNet, which is the most accurate anchorfree detector. To improve the outcomes of the upcoming image as well as detection efficiency, we disseminate the previously accurate long-term detection using a heatmap and a cascade guiding framework.
Result: On the COCO dataset, our technique obtains detection outcome AP50 -65.5%, AP! (Small Objects)-33.8, AP! (Medium Objects)-47.0 at 38 frames per second. The results of the study show the superiority of the suggested approach over the conventional one.
Conclusion: In this research, we have introduced an enhanced approach to image object detection using the improved T_CenterNet method. While the original CenterNet algorithm demonstrated superior precision compared to other single-stage algorithms, it suffered from slow detection speed. Our proposed approach, the Hourglass-208 model, built upon CenterNet, significantly improved detection performance. The proposed T_CenterNet algorithm improves upon the original CenterNet by incorporating the Hourglass backbone and feature map fusion techniques, enabling enhanced object detections. Additionally, we applied the Smooth L1 loss function to object size estimation within T_CenterNet, greatly enhancing overall detection performance by improving object size regression.
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