AM-MSFF: A Pest Recognition Network Based on Attention Mechanism and Multi-Scale Feature Fusion

Traditional methods for pest recognition have certain limitations in addressing the challenges posed by diverse pest species, varying sizes, diverse morphologies, and complex field backgrounds, resulting in a lower recognition accuracy. To overcome these limitations, this paper proposes a novel pest recognition method based on attention mechanism and multi-scale feature fusion (AM-MSFF). By combining the advantages of attention mechanism and multi-scale feature fusion, this method significantly improves the accuracy of pest recognition. Firstly, we introduce the relation-aware global attention (RGA) module to adaptively adjust the feature weights of each position, thereby focusing more on the regions relevant to pests and reducing the background interference. Then, we propose the multi-scale feature fusion (MSFF) module to fuse feature maps from different scales, which better captures the subtle differences and the overall shape features in pest images. Moreover, we introduce generalized-mean pooling (GeMP) to more accurately extract feature information from pest images and better distinguish different pest categories. In terms of the loss function, this study proposes an improved focal loss (FL), known as balanced focal loss (BFL), as a replacement for cross-entropy loss. This improvement aims to address the common issue of class imbalance in pest datasets, thereby enhancing the recognition accuracy of pest identification models. To evaluate the performance of the AM-MSFF model, we conduct experiments on two publicly available pest datasets (IP102 and D0). Extensive experiments demonstrate that our proposed AM-MSFF outperforms most state-of-the-art methods. On the IP102 dataset, the accuracy reaches 72.64%, while on the D0 dataset, it reaches 99.05%.

Insect recognition based on complementary features from multiple views

Insect pest recognition has always been a significant branch of agriculture and ecology. The slight variance among different kinds of insects in appearance makes it hard for human experts to recognize. It is increasingly imperative to finely recognize specific insects by employing machine learning methods. In this study, we proposed a feature fusion network to synthesize feature presentations in different backbone models. Firstly, we employed one CNN-based backbone ResNet, and two attention-based backbones Vision Transformer and Swin Transformer to localize the important regions of insect images with Grad-CAM. During this process, we designed new architectures for these two Transformers to enable Grad-CAM to be applicable in such attention-based models. Then we further proposed an attention-selection mechanism to reconstruct the attention area by delicately integrating the important regions, enabling these partial but key expressions to complement each other. We only need part of the image scope that represents the most crucial decision-making information for insect recognition. We randomly selected 20 species of insects from the IP102 dataset and then adopted all 102 kinds of insects to test the classification performance. Experimental results show that the proposed approach outperforms other advanced CNN-based models. More importantly, our attention-selection mechanism demonstrates good robustness to augmented images.

Inner-imaging 3D attention module for residual network

We propose an Inner-Imaging three-dimensional (3D) attentional feature fusion module for a residual network, which is a simple yet effective approach for residual networks. In our attention module, we constructed a 3D soft attention feature map to refine the input feature. The map fuses the attentional features from different dimensions, including channel and spatial axes, to create a 3D attention map. Then, we implemented a feature fusion module to further fuse the attentional features. Lastly, the attention module outputs a 3D soft attention map that is applied to the residual branch. The attention module can also model the relationship between attentional features from different dimensions and achieve the interaction between attentional features. This function allows our attention module to acquire more attentional features. To demonstrate the effectiveness of our method, extensive experiments were conducted on several computer vision benchmark datasets, including ImageNet 2012 and Microsoft COCO (MS COCO) 2017 datasets. The experimental results show that our method performed better than the baseline methods in the tasks of image classification, object detection, and instance segmentation tasks.