Abstract: To address the problems of many parameters and slow recognition speed of existing target recognition models,an improved lightweight target detection algorithm Yolov5s-MCB is proposed.Firstly,MobileNetV3 network is used as the Yolov5s backbone feature extraction network to reduce the number of parameters of the model.In order to fit the nonlinear data better and optimize the model convergence effecter,the MobileNetV3 network frontal ReLU activation function is replaced by Mish activation function to avoid gradient disappearance and gradient explosion.Secondly,the BiFPN feature pyramid structure is added to improve the detection accuracy with an iterative feature fusion method.In addition,the introduction of coordinate attention mechanism allows the model to focus on a wide range of location information to improve the detection performance.In order to optimize the model training rate of convergence,Focal-Loss EIOU is used as the border regression loss function to solve the problem of low-quality samples generating drastic oscillations in loss values.The experimental results show that the algorithm achieves an average recognition accuracy of 90.5% in the VOC dataset,a model size of 7.63 MB,and a detection speed of 99 FPS.Compared with Yolov5s,the proposed algorithm improves the inference speed by 17.85% and reduces the model size by 45.9% while keeping the recognition accuracy unchanged,meeting the requirements of the real-time detection tasks and detection accuracy.And the Yolov5s-MCB model is converted to ONNX model and ported to a cell phone to develop an AR application with target detection function in combination with ARCore SDK.