In autonomous driving, accurately identifying traffic targets is crucial for ensuring the safe and reliable operation of autonomous vehicles. Millimeter-wave radar, known for its low cost, long detection range, and excellent performance under various weather conditions. Deep learning algorithms, particularly the radar object detection network (RODNet), have been effectively applied to radar target detection by analyzing the range-azimuth (RA) heatmaps that capture complex target features. However, the low angular resolution of radar RA heatmaps, combined with the high sensitivity of millimeter-wave radar to metal objects, makes adjacent targets prone to misdetection and increases the likelihood of misclassification of target types due to metal reflections from road obstacles. To address these issues, this paper proposes an innovative extension suppression method to enhance RA heatmaps, reducing interference between adjacent targets and significantly improving target resolution. Additionally, the paper incorporates Gaussian filtering, peak detection, and amplitude suppression algorithms to design an interference suppression method, accurately identifying and mitigating strong reflections from non-target regions, thereby improving detection efficiency in complex environments. The effectiveness and superiority of these methods have been fully validated, with AP improvements of 18% in overlapping scenarios, 2% in metal obstacle scenarios, and around 10% in high-speed scenarios compared to the latest methods.