改进的 YOLOv3 的红外目标检测算法

复杂背景下红外多目标图像及视频的检测是目标检测的热点也是难点,为了更准确地检测出复杂背景下的红外目标, 将 YOLOv3 算法进行改进,首先通过在算法的原有基础上增加特征尺度,提高对距离远且背景复杂的待测图像的识别精度,并 将 BN 网络层与卷积神经网络层融合计算得到最后的检测结果,将原来的 YOLOv3 算法与改进后的算法的结果进行分析对比 可得,改进后的算法能够将平均识别精度从 64%提高到 88%,将 mAP 从 51. 73 提高到 59. 28,验证了改进后的 YOLOv3 算法在 红外目标检测下具有更好的性能,更明显的优势。

Improved infrared target detection algorithm of YOLOv3

The detection of infrared multi-target Image and video in complex background is the hotspot and difficulty of target detection. In order to detect infrared target in complex background more accurately, the algorithm of YOLOv3 is improved. Firstly, by increasing the feature scale on the basis of the original algorithm, the recognition accuracy of remote and complex background image is improved, and the BN network layer and convolution neural network are combined. The final detection results are obtained by layer fusion calculation. The analysis and comparison between the original algorithm and the improved algorithm show that the improved algorithm can better the average recognition accuracy from 64% to 88%, and the mAP from 51. 73 to 59. 28, which is verified that the improved YOLOv3 algorithm has better performance and more obvious advantages in infrared target detection.