  Detecting objects across various scales remains a significant challenge in
computer vision, particularly in tasks such as Rice Leaf Disease (RLD)
detection, where objects exhibit considerable scale variations. Traditional
object detection methods often struggle to address these variations, resulting
in missed detections or reduced accuracy. In this study, we propose the
multi-scale Attention Pyramid module (mAPm), a novel approach that integrates
dilated convolutions into the Feature Pyramid Network (FPN) to enhance
multi-scale information ex-traction. Additionally, we incorporate a global
Multi-Head Self-Attention (MHSA) mechanism and a deconvolutional layer to
refine the up-sampling process. We evaluate mAPm on YOLOv7 using the MRLD and
COCO datasets. Compared to vanilla FPN, BiFPN, NAS-FPN, PANET, and ACFPN, mAPm
achieved a significant improvement in Average Precision (AP), with a +2.61%
increase on the MRLD dataset compared to the baseline FPN method in YOLOv7.
This demonstrates its effectiveness in handling scale variations. Furthermore,
the versatility of mAPm allows its integration into various FPN-based object
detection models, showcasing its potential to advance object detection
techniques.
