In order to solve the problem of low accuracy caused by large classification loss in the lightweight target detection algorithm, a method of detecting the location and classification of the target with double detection heads is proposed. In the algorithm, the convolution head is used to detect the position, and the full connector is used to detect the classification. In the classification detection, after the feature map passes through the convolution layer, the feature map of the fused position regression branch is processed through the full connection layer. A grouping full connection method is proposed to further reduce the amount of calculation in the full connection layer. The algorithm is trained in VOC datasets. The results show that the classification loss of the improved model is significantly reduced, and the detection accuracy of the lightweight target detection algorithm is effectively improved. The accuracy of the algorithm on the VOC test set has reached 70.08% mAP.