The existing pedestrian detection suffers the low accuracy when the environment changes dramatically. In order to solve the problem, a pedestrian detection model combining information fusion and multi-scale detection is proposed. The model is composed of a retinex algorithm and an improved YOLOv3 algorithm. Retinex algorithm is selected as the preprocessing algorithm to improve the brightness and contrast of pedestrians. The model improves the YOLOv3 algorithm by adding multiple scale detections. The K-means is used to determine the number of optimal anchors and the aspect ratio. By testing on the standard data set, the mean average precision (mAP) of the joint detection model increases from the original 80.69–91.07%, and the recall increases from 65.22 to 87.48%. The comparative experiments show that the improved model performs good robustness and generalization ability on the problem of low pedestrian detection accuracy in complex environments.

中文翻译：

---

用于行人检测的联合信息融合和多尺度网络模型

当环境发生剧烈变化时，现有的行人检测精度较低。针对这一问题，提出了一种信息融合与多尺度检测相结合的行人检测模型。该模型由retinex算法和改进的YOLOv3算法组成。选择 Retinex 算法作为预处理算法，以提高行人的亮度和对比度。该模型通过添加多尺度检测改进了 YOLOv3 算法。K-means 用于确定最佳锚点的数量和纵横比。通过在标准数据集上进行测试，联合检测模型的平均精度（mAP）从原来的80.69-91.07%提高，召回率从65.22提高到87.48%。