ESA-Net: An efficient scale-aware network for small crop pest detection

Pest detection aims to locate and classify the pests that may be present in the image, which plays a crucial role in the early warning of pests linked to the agriculture industry. However, crop pests are generally very small and uneven in scale, resulting in poor detection results. To address this challenge, we propose the Efficient ScaleAware Network (ESA-Net), which comprises three core components: a High-Level Semantic Feature Extraction Module (HSFEM), a Low-Level Feature Enhancement Module (LFEM), and a Dynamic Scale-Aware Head (DSH). To avoid network optimization confusion, we use the HSFEM to extract high-level features that build the feature pyramid networks. Then, the LFEM is designed to further optimize the integrated feature map of low-level features, providing more fine-gained information for feature extraction and improving the detector's ability to differentiate between pests with similar appearances. Additionally, the proposed DSH can improve the detection performance of small objects by adaptively selecting the appropriate detection receptive field in accordance with various object scales. Experiment results on LMPD2020 dataset (68.8% mAP) and APHIDc dataset (75.3% mAP) demonstrate that ESA-Net achieves competitive results compared to the state-of-the-art methods.