A Feature Fusion Network With Multiscale Adaptively Attentional for Object Detection in Complex Traffic Scenes

Currently, small object detection in complex traffic scenarios remains a major challenge due to variations in object shape, scales, and external environment, as well as a high inter-class similarity. Although the latest algorithms achieve high accuracy, the results for small objects are still unsatisfactory. To obtain satisfactory small object detection results, the YOLOv8 algorithm is used to construct a feature fusion network with multiscale adaptively attentional (MAAFFNet). Firstly, the C2f module is modified to replace the simple summation operation with Multiscale Iterative Attentional Feature Fusion (MSIAFF), which enhances the ability of multi-scale aggregation of the C2f module and suppresses the interference of irrelevant information, resulting in a richer semantic feature map. Secondly, the adaptive spatial feature fusion (ASFF) module is introduced to deal with the feature maps of different scales, allocate the weights of spatial features of different levels, and enhance the consistency of semantic information between each layer of the feature maps, so as to improve the detail richness of the network. Finally, to achieve more accurate classification of small objects, a Slide loss function is introduced to achieve object classification and localization. Our proposed algorithm has been extensively experimented on KITTI, COCO-traffic and VOC 2012 datasets, respectively, comparing the proposed method to popular two-stage and one-stage object detection algorithms, the proposed method performs well in terms of accuracy.