LCFANet: A Novel Lightweight Cross-Level Feature Aggregation Network for Small Agricultural Pest Detection

In agricultural pest detection, the small size of pests poses a critical hurdle to detection accuracy. To mitigate this concern, we propose a Lightweight Cross-Level Feature Aggregation Network (LCFANet), which comprises three key components: a deep feature extraction network, a deep feature fusion network, and a multi-scale object detection head. Within the feature extraction and fusion networks, we introduce the Dual Temporal Feature Aggregation C3k2 (DTFA-C3k2) module, leveraging a spatiotemporal fusion mechanism to integrate multi-receptive field features while preserving fine-grained texture and structural details across scales. This significantly improves detection performance for objects with large scale variations. Additionally, we propose the Aggregated Downsampling Convolution (ADown-Conv) module, a dual-path compression unit that enhances feature representation while efficiently reducing spatial dimensions. For feature fusion, we design a Cross-Level Hierarchical Feature Pyramid (CLHFP), which employs bidirectional integration—backward pyramid construction for deep-to-shallow fusion and forward pyramid construction for feature refinement. The detection head incorporates a Multi-Scale Adaptive Spatial Fusion (MSASF) module, adaptively fusing features at specific scales to improve accuracy for varying-sized objects. Furthermore, we introduce the MPDINIoU loss function, combining InnerIoU and MPDIoU to optimize bounding box regression. The LCFANet-n model has $2.78M$ parameters and a computational cost of $6.7$ GFLOPs, enabling lightweight deployment. Extensive experiments on the public dataset demonstrate that the LCFANet-n model achieves a precision of $71.7\%$, recall of $68.5\%$, mAP50 of $70.4\%$, and mAP50-95 of $45.1\%$, reaching state-of-the-art (SOTA) performance in small-sized pest detection while maintaining a lightweight architecture.