A Deep-Learning Detector via Optimized YOLOv7-bw Architecture for Dense Small Remote-Sensing Targets in Harsh Food Supply Applications

With the progressive advancement of remote sensing image technology, its application in the agricultural domain is becoming increasingly prevalent. Both cultivation and transportation processes can greatly benefit from utilizing remote sensing images to ensure adequate food supply. However, such images often exist in harsh environments with many gaps and dense distribution, which poses major challenges to traditional target detection methods. The frequent missed detections and inaccurate bounding boxes severely constrain the further analysis and application of remote sensing images within the agricultural sector. This study presents an enhanced version of the YOLO algorithm, specifically tailored to achieve high-efficiency detection of densely distributed small targets in remote sensing images. We replaced the convolutions with a convolution kernel size of 3 in the last two ELAN modules with DeformableConvNetsv2 so that the backbone can better extract various objects. The proposed detector introduces a Bi-level Routing Attention module to the pooled pyramid SPPCSPC network of YOLOv7, thereby intensifying the attention towards areas of target concentration and augmenting the network's capacity to extract features related to dense small targets through effective feature fusion. Additionally, our approach employs a dynamic non-monotonic WIoUv3 to ensure the loss function of the network, enabling the allocation of the most appropriate gradient gain strategy at each instant and enhancing the network's ability to focus on detecting targets accurately. Finally, through comparative experimentation on the DIOR remote sensing image dataset, our proposed YOLOv7-bw exhibits superior performance with higher [email protected] and [email protected]: 0.95, achieving detection rates of 85.63% and 65.93%, surpassing those of the YOLOv7 detector by 1.93% and 2.03%, respectively, thus substantiating the effectiveness of our algorithmic approach.