【摘要】 针对Tiny YOLOV3目标检测算法在实时检测中对行人等小目标漏检率高的问题,对该算法的特征提取网络、预测网络、损失函数等进行研究改进。首先,在特征提取网络中增加2步长的卷积层,代替原网络中的最大池化层进行下采样;接着,使用深度可分离卷积构造反残差块替换传统卷积,降低模型尺寸和参数量,增加高维特征提取;然后,在原网络两尺度预测的基础上增加一尺度,形成三尺度预测;最后,对损失函数中的边界框位置误差项进行优化。实验结果表明,改进后的Tiny YOLOV3算法的目标检测准确率比原算法提高了9.8%,满足实时性要求,具有一定鲁棒性。本文方法能够更好地提取目标特征,多尺度预测和边界框位置误差的改进能更准确地对目标进行检测。更多还原

【Abstract】 The Tiny YOLOV3 target detection algorithm has a high error rate for small targets,such as pedestrians,in real-time detection.Therefore,this study aimed to improve the feature extraction network,prediction network,and loss function of the algorithm.First,a two-step convolution layer was added to the feature extraction network to replace the maximum pooling layer in the original network for downsampling.Second,the traditional convolution was replaced with an anti-residual block constructed by a deep convolutional convolution to reduce the model size as well as number of parameters and increase the high-dimensional feature extraction.Third,based on the original two-scale prediction of the network,a scale was added to form a three-scale prediction.Finally,the boundary box position error in the loss function was optimized.The experimental results demonstrat that the improved Tiny YOLOV3 algorithm achieve a target detection accuracy that IS 9.8% higher than the original algorithm,satisfied the real-time requirement,and demonstratedrobustness.The proposed method can better extract target features,and the multi-scale prediction and improvement of the boundary box position error can detect targets more accurately.更多还原