Multi-angle truck model recognition based on detail awareness

A detail-aware multi-angle vehicle recognition algorithm is proposed to address the problem of detail information loss due to pooling operation in multi-angle vehicle recognition. Firstly, considering that the differences between different vehicles are concentrated in the vehicle length and axle regions, the mid-level features are selected to build a local perception module, and the ECA attention mechanism is embedded to enhance the network's discrimination of details in local regions and suppress the interference of low discrimination features; secondly, a void space pyramid pooling model is introduced to increase the perceptual field to aggregate multi-scale contextual information, which reduces the problem of detail information loss and improves the global The second is the introduction of the null space pyramid pooling model, which increases the perceptual field to aggregate multi-scale contextual information, reduces the loss of detail information and improves the perception of global features. The experimental results show that the detection accuracy mAP of the improved algorithm reaches 96.1%, and the proposed method can effectively obtain local features with discrimination and improve the perception ability of the global features.