This paper proposes a lightweight model combined with data augmentation for vehicle detection in an intelligent sensor system. Vehicle detection can be considered as a binary classification problem, vehicle or non-vehicle. Deep neural networks have shown high accuracy in audio classification, and convolution neural networks are widely used for audio feature extraction and audio classification. However, the performance of deep neural networks is highly dependent on the availability of large quantities of training data. Recordings such as tracked vehicles are limited, and data augmentation techniques can be applied to improve the overall detection accuracy. In our case, spectrogram augmentation is applied on the mel spectrogram before extracting the Mel-scale Frequency Cepstral Coefficients (MFCC) features to improve the robustness of the system. Then depthwise separable convolution is applied to the CNN network for model compression and migrated to the hardware platform of the intelligent sensor system. The proposed approach is evaluated on a dataset recorded in the field using intelligent sensor systems with microphones. The final frame-level accuracy achieved was 94.64% for the test recordings and 34% of the parameters were reduced after compression.

Recent deep learning-based face recognition (FR) methods have demonstrated remarkable performance in high-resolution (HR) or down-sampled low-resolution (LR) tasks. However, these methods often exhibit disappointing speed-accuracy tradeoffs when deployed on real-world LR scenarios due to limited model generalization. To this end, we propose a lightweight Face Transformer framework for real-world LRFR named LLRFaceFormer. First, we propose a Transformers-as-Convolutions (TaC) network using a Transformer layer to replace the matrix multiplication in the standard convolutional process. This hybrid approach combines the strengths of Transformers and CNNs, allowing the TaC network to extract sufficient effective identity information via a global receptive field while adaptively discarding redundant homogeneous identity information on constructed LR faces. Second, we propose a Transformer-specific adaptive average procedure that incorporates tensor shape operations and a depthwise (DW) convolution. This procedure enables the LLRFaceFormer framework to focus on different regions of the input images. We also introduce an identity-aware simulator that generates real-world-like blurred LR scenarios during the training process to reduce the distribution discrepancy between the training LR faces and the testing real-world LR faces. The identity-aware simulator is simultaneously trained with the FR network with a cooperative training strategy. Further experiments illustrate the significantly superior speed-accuracy tradeoffs over existing LRFR methods with state-of-the-art (SOTA) performance on several LRFR benchmarks.

Currently, various studies have been exploring generation of long videos. However, the generated frames in these videos often exhibit jitter and noise. Therefore, in order to generate the videos without these noise, we propose a novel framework composed of four modules: separate tuning module, average fusion module, combined tuning module, and inter-frame consistency module. By applying our newly proposed modules subsequently, the consistency of the background and foreground in each video frames is optimized. Besides, the experimental results demonstrate that videos generated by our method exhibit a high quality in comparison of the state-of-the-art methods.