基于改进 Faster R-CNN 的棉布包装缺陷检测的 方法研究

由于传统检测算法对棉布包装缺陷检测不够准确、对小目标缺陷识别率不够高，所以提出改进的Faster R-CNN深度学习网络，对棉布包装存在的破损、污渍、孔洞、杂质、线头等5种缺陷进行检测。通过对图像进行预处理实现图像增强，然后改进Faster R-CNN中的RPN和ROI结构，为加强小目标缺陷的检测能力，在主干网络中融合特征金字塔网络结构，最后对ROI进行双线性插值以解决多次量化引起的像素偏差问题。实验表明，改进后的网络对棉布包装表面缺陷检测的平均精度均值mAP为91.34%,与传统算法相比，mAP值提高了9.08%。

Research on cotton packaging defect detection method based on improved Faster R-CNN

Because the traditional detection algorithm is not accurate enough to detect cotton packaging defects and the recognition rate of small target defects is not high enough, an improved Faster R-CNN deep learning network is proposed to detect five defects such as damage, stain, hole, impurity and thread end in cotton packaging. Image enhancement is realized by preprocessing the image, then the RPN and ROI structure in Faster R-CNN are improved. In order to strengthen the detection ability of small target defects, the feature pyramid network structure is fused in the backbone network, and finally the ROI is bilinear interpolated to solve the problem of pixel deviation caused by multiple quantization. Experiments show that the average accuracy of the improved network for cotton packaging surface defect detection is 91. 34%, which is 9. 08% higher than the traditional algorithm.