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6 May 2022 Object detection algorithm based on lightweight convolutional neural networks for mobile devices
Xuan Chen
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Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121760O (2022) https://doi.org/10.1117/12.2636421
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China
Abstract
Based on the traditional SSD(Single Shot multibox Detection) object detection algorithm, this paper replaces the largescale VGG feed-forward network with the lightweight depthwise separable mixed convolutional neural network Mixmobilenet ,proposed in this paper, which makes the overall object detection model parameters volume decreased. Then add multi-scale feature fusion method FPN (Feature Pyramid Network) and focal loss function FL (Focal Loss). Based on the above, this paper innovatively proposes a lightweight depthwise separable convolutional object detection algorithm Mixmobilenet-FFSSD. Experiments show that the accuracy rate on the PASCAL VOC dataset is 72.5m AP, and the parameter quantity is only 6.6M. The object detection algorithm has good performance in terms of parameter quantity, accuracy and real-time performance, and is suitable for the efficient object detection requirements of embedded smart devices.
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Xuan Chen "Object detection algorithm based on lightweight convolutional neural networks for mobile devices", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121760O (6 May 2022); https://doi.org/10.1117/12.2636421
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KEYWORDS
Detection and tracking algorithms
Convolution
Convolutional neural networks
Mobile devices
Evolutionary algorithms
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