Adversarial training, a key strategy for enhancing the adversarial robustness of deep neural network (DNNs), has been widely studied in image classification but lacks sufficient research in object detection. Traditional adversarial training often relies on projected gradient descent (PGD) for robust optimization of models. However, the iterative process of generating adversarial examples greatly prolongs model training, becoming a major bottleneck for deploying adversarial training in computationally intensive tasks like object detection. To address this, this study proposes an adversarial training method based on Nesterov’s accelerated gradient (NAG). By introducing the NAG momentum mechanism, algorithm convergence is accelerated. This method maintains detection accuracy comparable to PGD-trained models while significantly improving adversarial training efficiency. In addition, the main difference between object detection and image classification lies in object bounding box localization. However, it is observed that existing methods still focus on learning adversarial examples generated from classification loss, while neglecting the particularity of localization in object detection. To address this, an adaptive loss re-weighting strategy is designed to balance the number of adversarial examples derived from different tasks during training, thus enabling the model to focus on localization to enhance robustness. Experiments on the PASCAL VOC and MS-COCO datasets demonstrate the effectiveness of the proposed method compared with existing advanced adversarial training approaches for object detection.