Detection of small targets in cucumber disease images through global information perception and feature fusion - Wang, Xuewei; Liu, Jun (Frontiers in Sustainable Food Systems, 2024)
The cucumber disease images obtained from natural environments often contain noise such as variations in lighting and soil conditions, which significantly impact the accuracy of disease recognition. Additionally, existing detection models require large memory footprints, making real-time cucumber disease detection challenging. To address the challenges associated with detecting small targets in cucumber disease images, this study presents an algorithm named CucumberDet, which integrates global information perception and feature fusion. Initially, we employ the Swin Transformer as the backbone network for RetinaNet to augment the primary network's feature extraction capabilities, thus enhancing its ability to extract information globally. Subsequently, to strengthen the network's detection capabilities, especially for remote and small targets, we introduce a highly effective Small Target Feature Fusion Module (SFFM) to meticulously integrate detailed data of small targets into shallow feature maps. Finally, to further refine the network's capability to identify multi-scale targets and facilitate the flow of low-level feature information to high-level features, we introduce a novel Multi-level Feature Adaptive Fusion Module (MFAFM). Encouraging detection results are obtained across three distinct datasets, with experimental findings on a self-compiled cucumber disease image dataset revealing that our proposed algorithm improves detection accuracy by 6.8% compared to the original RetinaNet baseline network. The proposed model achieves an mAP of 92.5%, with a parameter count of 38.39 million and a frame per second (FPS) rate of 23.6, underscoring its superior performance in detecting small targets and demonstrating its effectiveness across various application scenarios.