SPCNet: Serial Pyramid Convolutional Network for Remote Sensing Object Detection

Recently, remote sensing object detection (RSOD) has attracted increasing attention. Despite advancements in existing methods, challenges like high computational costs, large object scale variations, and difficulties in detecting small objects still remain. Many studies have attempted to address these issues by expanding the reception field but still struggle with multi-scale targets. To address these challenges, we propose SPCNet, an efficient multi-scale convolutional network that enhances feature maps by combining serial convolutions with a pyramid structure. First, we have designed a lightweight Serial Pyramid Convolutional (SPC) block that uses depthwise separable convolutions with varying dilation rates to capture multi-scale features and a pyramid structure to mitigate feature information loss caused by stacked convolutions. This mechanism is particularly effective at capturing intricate spatial relationships and at focusing on salient regions. Additionally, we have developed a novel backbone module, named SPC Stage, which integrates multiple SPC blocks along with a parallel large-kernel Feed-Forward Network (FFN) to aggregate multi-scale and wide-range contextual information. To confirm its effectiveness, we have evaluated our method on three widely used datasets named DOTA-v1.0, DOTA-v1.5, and HRSC2016. The extensive experiments on these datasets show that our SPCNet achieves the state-of-the-art performance with minimal parameters (72.48% mAP, 78.42% mAP, and 70.60% mAP, respectively). Our code will be made publicly available on https://github.com/FitzroyWangzj/SPCNet.