Abstract: Disclosed is a real-time object detection method deployed on a platform with limited computing resources, which belongs to the field of deep learning and image processing. In the present invention, YOLO-v3-tiny neural network is improved, Tinier-YOLO reserves the front five convolutional layers and pooling layers of YOLO-v3-tiny and makes prediction at two different scales. Fire modules in SqueezeNet, 1×1 bottleneck layers, and dense connection are introduced, so that the structure is used to achieve smaller, faster, and more lightweight network that can be run in real time on an embedded AI platform. The model size of Tinier-YOLO in the present invention is only 7.9 MB, which is only ¼ of 34.9 MB of YOLO-v3-tiny, and ? of YOLO-v2-tiny. The reduction in the model size of Tinier-YOLO does not affect real-time performance and accuracy of Tinier-YOLO. Real-time performance of Tinier-YOLO in the present invention is 21.8% higher than that of YOLO-v3-tiny and 70.8% higher than that of YOLO-v2-tiny.

Abstract: Disclosed is a real-time object detection method deployed on a platform with limited computing resources, which belongs to the field of deep learning and image processing. In the present invention, YOLO-v3-tiny neural network is improved, Tinier-YOLO reserves the front five convolutional layers and pooling layers of YOLO-v3-tiny and makes prediction at two different scales. Fire modules in SqueezeNet, 1×1 bottleneck layers, and dense connection are introduced, so that the structure is used to achieve smaller, faster, and more lightweight network that can be run in real time on an embedded AI platform. The model size of Tinier-YOLO in the present invention is only 7.9 MB, which is only ¼ of 34.9 MB of YOLO-v3-tiny, and ? of YOLO-v2-tiny. The reduction in the model size of Tinier-YOLO does not affect real-time performance and accuracy of Tinier-YOLO. Real-time performance of Tinier-YOLO in the present invention is 21.8% higher than that of YOLO-v3-tiny and 70.8% higher than that of YOLO-v2-tiny.