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Aiming at the serious problems of missed detection, false detection and difficult deployment of existing target detection algorithms when applied to traffic scenes, a vehicle detection model YOLOv8-FDD with lower parameter count and higher accuracy is proposed in this paper. First, an integrated Feature Sharing Detection Head is proposed to compress the amount of redundant parameters in the model head while ensuring the accuracy of the algorithm. Second, to enhance the interaction between regression features and classification features in the detection head, an Feature Dynamic Interaction Detection Head is further designed based on the Feature Sharing Detection Head. Third, the Dilation-wise Residual (DWR) module to strengthen the multi-scale feature extraction capability of the backbone network. Finally, the dynamic upsampling module DySample is utilized to improve the nearest neighbor interpolation upsampling in YOLOv8. Validation on the UA-DETRAC dataset shows that the proposed YOLOv8-FDD model’s parameter count is only 72.89% of the original YOLOv8, with computational complexity slightly increasing from 8.1 GFLOPs to 8.5 GFLOPs. The mAP50 and mAP50-95 improved by 0.7% and 1.3% respectively, with FPS consistently above 300. Additionally, on a self-built traffic surveillance dataset, YOLOv8-FDD outperformed the original algorithm. The error type statistics indicate that YOLOv8-FDD significantly reduces both vehicle false negatives and false positives, making it a high-accuracy, well-generalized real-time vehicle detection method.

Visual inspection of long-term electroencephalography (EEG) is a tedious task for physicians in neurology. Based on bidirectional gated recurrent unit (Bi-GRU) neural network, an automatic seizure detection method is proposed in this paper to facilitate the diagnosis and treatment of epilepsy. Firstly, wavelet transforms are applied to EEG recordings for filtering pre-processing. Then the relative energies of signals in several particular frequency bands are calculated and inputted into Bi-GRU network. Afterwards, the outputs of Bi-GRU network are further processed by moving average filtering, threshold comparison and seizure merging to generate the discriminant results that the tested EEG belong to seizure or not. Evaluated on CHB-MIT scalp EEG database, the proposed seizure detection method obtained an average sensitivity of 93.89% and an average specificity of 98.49%. 124 out of 128 seizures were correctly detected and the achieved average false detection rate was 0.31 per hour on 867.14 h testing data. The results show the superiority of Bi-GRU network in seizure detection and the proposed detection method has a promising potential in the monitoring of long-term EEG.