YOLOv8 is one of the most commonly used object detection algorithms. However, its network model has a large number of parameters, resulting in slow performance on embedded devices. One of the challenges in industrial applications of this algorithm is reducing the parameter size of the YOLOv8 model without significantly compromising its detection accuracy, thus enabling it to run efficiently on embedded devices. To address this, a structured pruning strategy based on Torch-Pruning was designed specifically for medium-sized YOLOv8 models like YOLOv8m.In this study, the model was trained on the COCO dataset, resulting in a computational workload of 39.6G and a parameter count of 25.9M. Thirteen pruning iterations were conducted with different pruning rates to systematically reduce the model's parameter count and identify the optimal pruned model. Comparative analysis with the unpruned model showed promising results: the computational workload decreased from 39.6G to 33.7G, a reduction of 14.9%; the parameter count decreased from 25.9M to 22.0M, a reduction of 15%; the average precision improved from 0.6 before pruning to 0.7 after fine-tuning the pruned model parameters; and the inference time per image decreased from 10.2ms before pruning to 9.5ms afterward.
At present, taxi is one of the main transportation modes for passengers to arrive at their destination, and its convenience is increasingly becoming an important transportation mode in many cities. However, taxis queue up for passengers or passengers queue up to take the bus from time to time. Based on this problem, this paper establishes a corresponding mathematical model to analyze the taxi driver's selection strategy after sending passengers to the airport, so that the taxi driver can improve the efficiency of passengers as much as possible, so as to increase the available income. Firstly, in order to further put forward the selection strategy of taxi drivers, this paper reflects the driver's income according to the passenger mileage, no-load mileage, no-load rate and passenger carrying rate of taxis in different periods, and makes a selection strategy for taxi drivers from the aspect of maximum income. Finally, in order to consider the fairness of the airport taxi industry and make the income of these taxis as balanced as possible, we analyze and study from the two aspects of "short distance vehicles" and "long distance vehicles", and solve the problem of "boarding points" by setting up non passable lanes to improve the efficiency of the airport.
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