DPAKS: Reliable DETR Guided by Prior Auxiliary Knowledge for Small Object Detection

Deep neural networks are highly effective at transforming sparse and unstructured data into dense and semantic representations, demonstrating strong capabilities in object detection tasks. However, their performance often diminishes when detecting small-sized objects due to the loss or corruption of critical information during feature extraction. To address this challenge, DPAKS is introduced, a reliable DETR- like detector for small objects enhanced with directional prior auxiliary knowledge to guide the model's focus on small objects. In the decoder of DPAKS, a small denoising training strategy is employed that reduces the interference of noisy queries generated from real small objects. This approach effectively learns the features of small objects during the denoising process, sharpening the model's attention to small-sized objects. Additionally, to enhance the reliability of DPAKS's backbone, an auxiliary branch is introduced that provides supervision via shorter paths, improving the optimization of low-level feature parameters. This branch facilitates the transmission of gradient information suited for small objects without interfering with the detection of other sized objects. Furthermore, a new supervision head is proposed and added to the detection head of DPAKS, which categorizes object sizes based on artificial prior knowledge. This guides the model to effectively learn size categories and become more sensitive to small objects. Remarkably, DPAKS achieves competitive performance in small object detection without imposing additional computational burdens at the inference stage. The code is available at https://github.com/XUhaozhi88/DPAKS.