Dual attention guided context-aware feature learning for residual unfilled grains detection on threshed rice panicles

Accurate detection of residual unfilled grains on threshed rice panicles is a critical step in determining a reliable grain-setting rate, and holds significant potential for the development of high-quality rice strains. Recent deep learning-based techniques have been actively explored for discerning various types of objects. However, this detection task is challenging, as many objects are densely occluded by branches or other unfilled grains. Additionally, some unfilled grains are closely adjacent and exhibit small sizes, further complicating the detection process. To address these challenges, this paper proposes a novel Channel-global Spatial-local Dual Attention (CSDA) module, aimed at enhancing feature correlation learning and contextual information embedding. Specifically, the channel- and spatial-wise attention are deployed on two parallel branches, and incorporated with the global and local representation learning paradigm, respectively. Furthermore, we integrate the CSDA module with the backbone of an object detector, and refine the loss function and detection head using the Focaler-SIoU and tiny object prediction head. This enables the object detector to effectively differentiate residual unfilled grains from occlusions, and at the meantime, focusing on the subtle differences between closely adjacent and small-sized unfilled grains. Experimental results show that our work achieves superior detection performance versus other competitors with an [email protected] of 95.3 % (outperforming rivals by 1.5–32.6 %) and a frame rate of 154 FPS (outperforming rivals by 12–132 FPS), enjoying substantial potentials for practical applications. • CSDA module enhances feature correlation and contextual embedding. • Framework integrates CSDA and Focaler-SIoU for robust occluded-grain detection. • Optimized detection head improves sensitivity to tiny grains.