  Object detection, a crucial aspect of computer vision, has seen significant
advancements in accuracy and robustness. Despite these advancements, practical
applications still face notable challenges, primarily the inaccurate detection
or missed detection of small objects. In this paper, we propose YOLO-TLA, an
advanced object detection model building on YOLOv5. We first introduce an
additional detection layer for small objects in the neck network pyramid
architecture, thereby producing a feature map of a larger scale to discern
finer features of small objects. Further, we integrate the C3CrossCovn module
into the backbone network. This module uses sliding window feature extraction,
which effectively minimizes both computational demand and the number of
parameters, rendering the model more compact. Additionally, we have
incorporated a global attention mechanism into the backbone network. This
mechanism combines the channel information with global information to create a
weighted feature map. This feature map is tailored to highlight the attributes
of the object of interest, while effectively ignoring irrelevant details. In
comparison to the baseline YOLOv5s model, our newly developed YOLO-TLA model
has shown considerable improvements on the MS COCO validation dataset, with
increases of 4.6% in<EMAIL_ADDRESS>and 4% in mAP@0.5:0.95, all while keeping the
model size compact at 9.49M parameters. Further extending these improvements to
the YOLOv5m model, the enhanced version exhibited a 1.7% and 1.9% increase in
<EMAIL_ADDRESS>and mAP@0.5:0.95, respectively, with a total of 27.53M parameters.
These results validate the YOLO-TLA model's efficient and effective performance
in small object detection, achieving high accuracy with fewer parameters and
computational demands.
