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Despite significant advances and innovations in deep network-based vehicle detection methods, finding a balance between detector accuracy and speed remains a significant challenge. This study aims to present an algorithm that can manage the speed and accuracy of the detector in real-time vehicle detection while increasing detector speed with accuracy comparable to high-speed detectors. To this end, the Fast-Yolo-Rec algorithm is proposed. The proposed method includes a new Yolo-based detection network and LSTM-based position prediction networks. The proposed semantic attention mechanism in the spatial semantic attention module (SSAM) significantly impacts accuracy and speed on par with the most recent fast detectors. Recurrent position prediction networks, on the other hand, improve the detection speed by estimating the current vehicle position using vehicle position history. The vehicle trajectories are classified, and the LSTM network for the specified trajectory predicts the vehicle positions. The Fast-Yolo-Rec algorithm not only determines the position of the vehicle faster than high-speed detectors but also allows for the speed control of the detection network with acceptable accuracy. The evaluation results on a large Highway dataset show that the proposed scheme outperforms the baseline methods.

Fast-Yolo-Rec: Incorporating Yolo-Base Detection and Recurrent-Base Prediction Networks for Fast Vehicle Detection in Consecutive Images