An Ochotona Curzoniae Object Detection Model Based on Feature Fusion with SCConv Attention Mechanism

The detection of Ochotona Curzoniae serves as a fundamental component for estimating the population size of this species and for analyzing the dynamics of its population fluctuations. In natural environments, the pixels representing Ochotona Curzoniae constitute a small fraction of the total pixels, and their distinguishing features are often subtle, complicating the target detection process. To effectively extract the characteristics of these small targets, a feature fusion approach that utilizes up-sampling and channel integration from various layers within a CNN can significantly enhance the representation of target features, ultimately improving detection accuracy. However, the top-down fusion of features from different layers may lead to information duplication and semantic bias, resulting in redundancy and high-frequency noise. To address the challenges of information redundancy and high-frequency noise during the feature fusion process in CNN, we have developed a target detection model for Ochotona Curzoniae. This model is based on a spatial-channel reconfiguration convolutional (SCConv) attentional mechanism and feature fusion (FFBCA), integrated with the Faster R-CNN framework. It consists of a feature extraction network, an attention mechanism-based feature fusion module, and a jump residual connection fusion module. Initially, we designed a dual attention mechanism feature fusion module that employs spatial-channel reconstruction convolution. In the spatial dimension, the attention mechanism adopts a separation-reconstruction approach, calculating a weight matrix for the spatial information within the feature map through group normalization. This process directs the model to concentrate on feature information assigned varying weights, thereby reducing redundancy during feature fusion. In the channel dimension, the attention mechanism utilizes a partition-transpose-fusion method, segmenting the input feature map into high-noise and low-noise components based on the variance of the feature information. The high-noise segment is processed through a low-pass filter constructed from pointwise convolution (PWC) to eliminate some high-frequency noise, while the low-noise segment employs a bottleneck structure with global average pooling (GAP) to generate a weight matrix that emphasizes the significance of channel dimension feature information. This approach diminishes the model’s focus on low-weight feature information, thereby preserving low-frequency semantic information while reducing information redundancy. Furthermore, we have developed a novel feature extraction network, ResNeXt-S, by integrating the Sim attention mechanism into ResNeXt50. This configuration assigns three-dimensional attention weights to each position within the feature map, thereby enhancing the local feature information of small targets while reducing background noise. Finally, we constructed a jump residual connection fusion module to minimize the loss of high-level semantic information during the feature fusion process. Experiments on Ochotona Curzoniae target detection on the Ochotona Curzoniae dataset show that the detection accuracy of the model in this paper is 92.3%, which is higher than that of FSSD512 (84.6%), TDFSSD512 (81.3%), FPN (86.5%), FFBAM (88.5%), Faster R-CNN (89.6%), and SSD512 (88.6%) detection accuracies.