An opto-electronic neural network is designed for video object detection from a long-exposure blurred image. This network combines an optical encoder, convolutional neural network decoder, and object detection module, which are jointly optimized end-to-end. The joint loss is adopted for updating the network according to the physical constraints of hardware via back-propagation. A high-speed refreshed spatial light modulator is used as the encoder part of the network to generate coded sub-images, and then, a single blurred image is obtained after a common camera. The rest of the network is used for video object detection. Both simulations and experiments demonstrate that our framework can successfully retrieve object labels and bounding boxes at different moments in the long exposure. To the best of our knowledge, this is the first work investigating video object detection from a single motion-degraded image.

中文翻译：

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通过光电神经网络从单个图像中检测视频对象

光电神经网络设计用于从长时间曝光的模糊图像中检测视频对象。该网络将端到端共同优化的光学编码器，卷积神经网络解码器和对象检测模块结合在一起。根据硬件的物理约束，通过反向传播采用联合损失来更新网络。高速刷新的空间光调制器用作网络的编码器部分，以生成编码的子图像，然后，在使用通用相机后获得单个模糊图像。网络的其余部分用于视频对象检测。仿真和实验均表明，我们的框架可以在长时间曝光的不同时刻成功检索对象标签和边界框。据我们所知，