A lightweight feature extraction backbone for gangue detection with improved Resnet50

In response to the sizable parameters and floating point operations (FLOPs) of feature extraction backbone of gangue detection algorithm, a lightweight backbone was proposed by improving Resnet50. Initially, the partial 3 × 3 convolution was used to replace regular 3 × 3 convolution in original Resnet50 for reducing parameters and FLOPs in convolution operation. Subsequently, layers of batch normalization and rectified linear unit after 3 × 3 convolution were removed for their further reduction. Finally, bottleneck attention module was integrated at the bottleneck of Resnet50 to improve the mean average precision (mAP) in gangue detection. Based on three proposed methods, the ablation and comparative experiments were carried out. The reduction in parameters and FLOPs and increase in mAP were analyzed and discussed according to experimental results. It could be concluded that the parameters and FLOPs of the obtained lightweight backbone were no more than 56.36% and 57.42% of those of the popular backbones, while the decrease amplitude of its mAP value was no more than 0.2%.