  Nowadays, there are more surgical procedures that are being performed using
minimally invasive surgery (MIS). This is due to its many benefits, such as
minimal post-operative problems, less bleeding, minor scarring, and a speedy
recovery. However, the MIS's constrained field of view, small operating room,
and indirect viewing of the operating scene could lead to surgical tools
colliding and potentially harming human organs or tissues. Therefore, MIS
problems can be considerably reduced, and surgical procedure accuracy and
success rates can be increased by using an endoscopic video feed to detect and
monitor surgical instruments in real-time. In this paper, a set of improvements
made to the YOLOV5 object detector to enhance the detection of surgical
instruments was investigated, analyzed, and evaluated. In doing this, we
performed performance-based ablation studies, explored the impact of altering
the YOLOv5 model's backbone, neck, and anchor structural elements, and
annotated a unique endoscope dataset. Additionally, we compared the
effectiveness of our ablation investigations with that of four additional SOTA
object detectors (YOLOv7, YOLOR, Scaled-YOLOv4 and YOLOv3-SPP). Except for
YOLOv3-SPP, which had the same model performance of 98.3% in mAP and a similar
inference speed, all of our benchmark models, including the original YOLOv5,
were surpassed by our top refined model in experiments using our fresh
endoscope dataset.
