Table 2.

Comparison with state-of-the-art models when selecting flame as the target, the model was compared with other YOLO variants and their derivatives in terms of mAP@0.5, mAP@0.5:0.95, parameter count, frame rate and recall rate.

Model	Param.(M)	mAP@0.5	mAP@0.5:0.95	Recall	FPS
FasterRCNN-resnet50	41.1	58.3	31.9	60.1	18.02
YOLOv3n	4.05	70.1	37.8	66.5	69.57
YOLOv3s	15.32	72.0	39.2	62.7	43.77
YOLOv5n	2.50	72.3	39.0	66.3	92.14
YOLOv5s	9.11	72.8	39.6	70.8	57.97
YOLOv6n	4.24	71.0	38.6	62.1	111.31
YOLOv6s	16.30	72.4	38.6	66.8	87.55
YOLOv8n	3.01	70.8	38.7	65.5	110.64
YOLOv8s	11.12	72.6	40.9	69.8	78.58
YOLOv9s	7.17	73.0	40.1	64.9	75.82
YOLOv10n	2.70	69.9	37.6	62.5	99.55
YOLOv10s	8.03	67.2	36.4	64.5	73.56
YOLO11n	2.96	73.1	39.6	67.6	122.11
YOLO11s	9.41	73.4	40.5	68.1	73.26
YOLOv12n	2.56	72.8	40.0	67.2	111.69
YOLOv12s	9.23	73.4	40.2	68.5	70.18
FBRT-YOLO-N	0.85	70.7	37.7	66.4	122.08
FBRT-YOLO-S	2.89	71.2	37.8	67.2	90.92
Gold-YOLO	8.04	72.3	39.8	66.1	71.52
RT-DETR-n	16.79	67.3	36.0	64.2	47.62
RT-DETR-resnet50	41.93	68.9	37.5	63.9	31.59
YOLO-GL(Ours)	4.29	74.3	41.6	70.4	80.59

All results were obtained without employing advanced training techniques such as knowledge distillation or PGI to ensure a fair comparison.