Comparative test results.

Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on millimeter wave radar object detection is of great practical significance for applications in the field of intelligent security and transportation. However, in the multi-target detection scene, millimeter wave radar still faces some problems, such as unable to effectively distinguish multiple objects and poor performance of detection algorithm. Focusing on the above problems, a new target detection and classification framework of S2DB-mmWave YOLOv8n, based on deep learning, is proposed to realize more accuracy. There are three main improvements. First, a novel backbone network was designed by incorporating new convolutional layers and the Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module to strengthen feature extraction. Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.

毫米波雷达（Millimeter-wave, mmWave）因其具备全天候、低成本、隐私性强且不受恶劣天气条件影响的特性，已成为目标检测领域的重要研究方向。因此，针对毫米波雷达目标检测的研究，在智能安防与交通领域的应用中具有重要的现实意义。然而，在多目标检测场景中，毫米波雷达仍面临诸多问题，例如无法有效区分多个目标、检测算法性能欠佳等。针对上述问题，本文提出了一种基于深度学习的新型目标检测与分类框架S2DB-mmWave YOLOv8n，以实现更高的检测与分类精度。该框架主要包含三项改进：其一，通过引入新型卷积层与简化空间金字塔池化-快速（Simplified Spatial Pyramid Pooling - Fast, SimSPPF）模块设计全新的骨干网络，强化特征提取能力；其二，引入动态上采样技术，提升模型恢复精细细节的能力；其三，集成双向特征金字塔网络（Bidirectional Feature Pyramid Network, BiFPN）以优化特征融合，借助双向信息传递机制与自适应特征选择策略。本次研究选用了一个公开的5分类毫米波雷达目标热力图数据集，包含2500张标注图像，用于数据建模与方法评估。实验结果表明，S2DB-mmWave YOLOv8n模型的平均精度均值（mean average precision, mAP）、精确率与召回率分别为：mAP@0.5下93.1%、mAP@0.5:0.95下55.8%、89.4%与90.6%；在未增加参数量的前提下，其性能较基线模型YOLOv8n分别提升了3.3、1.6、4.5与7.7个百分点。