  Traditional deep learning-based object detection networks often resize images
during the data preprocessing stage to achieve a uniform size and scale in the
feature map. Resizing is done to facilitate model propagation and fully
connected classification. However, resizing inevitably leads to object
deformation and loss of valuable information in the images. This drawback
becomes particularly pronounced for tiny objects like distribution towers with
linear shapes and few pixels. To address this issue, we propose abandoning the
resizing operation. Instead, we introduce Positional-Encoding Multi-head
Criss-Cross Attention. This allows the model to capture contextual information
and learn from multiple representation subspaces, effectively enriching the
semantics of distribution towers. Additionally, we enhance Spatial Pyramid
Pooling by reshaping three pooled feature maps into a new unified one while
also reducing the computational burden. This approach allows images of
different sizes and scales to generate feature maps with uniform dimensions and
can be employed in feature map propagation. Our SCAResNet incorporates these
aforementioned improvements into the backbone network ResNet. We evaluated our
SCAResNet using the Electric Transmission and Distribution Infrastructure
Imagery dataset from Duke University. Without any additional tricks, we
employed various object detection models with Gaussian Receptive Field based
Label Assignment as the baseline. When incorporating the SCAResNet into the
baseline model, we achieved a 2.1% improvement in mAPs. This demonstrates the
advantages of our SCAResNet in detecting transmission and distribution towers
and its value in tiny object detection. The source code is available at
https://github.com/LisavilaLee/SCAResNet_mmdet.
