关键词: 多尺度, 特征融合, 注意力机制, 矿区道路, 障碍检测, 数据增强

Abstract:

In order to solve the problem of travelling obstacle detection in the context of complex open pit mines, a mining road obstacle detection algorithm based on improved cross-scale feature fusion is proposed. Firstly, to address the problem of unbalanced small target sample categories in the original mine dataset, a data enhancement method based on geometric transformation and weighted Poisson fusion is used to expand the number of samples.Secondly, a cross-stage connectivity network that is more suitable for obstacle detection is proposed in the feature extraction stage to increase the detection scale and improve the algorithm's learning ability of the small target features, and then a 3D parameterless attention (SimAM) and de-weighted Bi-directional feature fusion pyramid network (Bi-FPN) are proposed in the feature fusion stage to improve the multi-scale detection performance by enlarging the predicted feature map and feature receptive field. Finally, to address the problems of sample imbalance and imprecise obstacle bounding box localisation in the training, the quality focal loss function (QFL) and the scalable Intersection and combination ratio loss function (SIoU), which combines the classification score with the quality prediction of the position to improve the localisation accuracy for dense occlusion targets. The results show that the improved method can effectively identify unstructured road obstacles in open pit mining area under complex background, and in practical application, the detection accuracy reaches 91.88% and the detection speed reaches 68.7 f/s, which has a better performance of small-target and multi-scale detection compared with the mainstream detection methods, and it can satisfy the requirements of obstacle safety detection in the travelling of unmanned mine cards in open pit mining area.

Key words: multi-scale, feature fusion, attention mechanism, mining roads, obstacle detection, data enhancement