Object Detection Algorithm Based on Improved Faster R-CNN

Fig. 7. Detection results with multiple targets overlapping. (a) Faster R-CNN; (b) add Soft-NMS+crop_and_resize; (c) add data enhancement; (d) our algorithm

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Table 1. Test results on the PASCAL VOC2007

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Table 1. Test results on the PASCAL VOC2007

Algorithm	Backbone	Training set	Testing set	mAP /%
Fast R-CNN	VGG-16	VOC2007	VOC2007	66.90
Faster R-CNN	VGG-16	VOC2007	VOC2007	69.90
SSD300	VGG-16	VOC2007	VOC2007	68.00
YOLO	GoogleNet	VOC2007	VOC2007	63.40
Data enhancement	VGG-16	VOC2007	VOC2007	70.90
Soft-NMS+crop_and_resize	VGG-16	VOC07++	VOC2007	73.10
Ours	VGG-16	VOC07++	VOC2007	76.40

Table 2. Detection results on PASCAL VOC07 ++ data set at different scales
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Table 2. Detection results on PASCAL VOC07 ++ data set at different scales
Enter size Backbone Training set Testing set mAP /%
128²,256²,521² VGG-16 VOC07++ VOC2007 76.40
64²,128²,256²,521² VGG-16 VOC07++ VOC2007 77.69
32²,64²,128²,256²,521² VGG-16 VOC07++ VOC2007 77.63

Table 3. Test results on PASCAL VOC07+12 test set

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Table 3. Test results on PASCAL VOC07+12 test set

Algorithm	Backbone	Training set	Testing set	mAP /%
Fast R-CNN	VGG-16	VOC07+12	VOC2007	70.00
Faster R-CNN	VGG-16	VOC07+12	VOC2007	73.20
Faster R-CNN	ResNet-101	VOC07+12	VOC2007	76.40
MR-CNN	ResNet-101	VOC07+12	VOC2007	78.20
ION	VGG-16	VOC07+12	VOC2007	76.50
YOLO	GoogleNet	VOC07+12	VOC2007	63.40
YOLOV2	Darknet-19	VOC07+12	VOC2007	78.60
SSD300	VGG-16	VOC07+12	VOC2007	77.20
Data enhancement	VGG-16	VOC07+12	VOC2007	75.80
Soft-NMS+crop_and_resize	VGG-16	VOC07+++12	VOC2007	78.40
Ours	VGG-16	VOC07+++12	VOC2007	81.20

Table 4. Detection results on PASCAL VOC07+++12 at different scales
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Table 4. Detection results on PASCAL VOC07+++12 at different scales
Enter size Backbone Training set Testing set mAP /%
128²,256²,521² VGG-16 VOC07+++12 VOC2007 81.22
64²,128²,256²,521² VGG-16 VOC07+++12 VOC2007 83.00
32²,64²,128²,256²,521² VGG-16 VOC07+++12 VOC2007 82.94

Table 5. mAP of different algorithms on COCO2014unit:%

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Table 5. mAP of different algorithms on COCO2014unit:%

Algorithm	Training set	IOU			Image size
Algorithm	Training set	0.50∶0.95	0.50	0.75	S	M	L
Fast R-CNN	train	19.70	35.90	-	-	-	-
Faster R-CNN	train	20.50	39.90	19.40	4.10	20.00	35.80
Faster R-CNN	train	21.90	42.70	-	-	-	-
ION^[18]	train	23.60	43.20	23.60	6.40	24.10	38.30
Faster R-CNN	trainval35	24.20	45.30	23.50	7.70	26.40	37.10
SSD300	trainval35	23.20	41.20	23.40	5.30	23.20	39.60
SSD512	trainval35	26.80	46.50	27.80	9.00	28.90	41.90
YOLOV2^[19]	trainval35	21.60	44.00	19.20	5.00	22.40	35.50
Ours	trainval35	26.60	47.20	27.00	11.40	30.80	37.10

Table 6. mAR of different algorithms on COCO2014unit:%

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Table 6. mAR of different algorithms on COCO2014unit:%

Algorithm	Training set	Number of iterations			Image size
Algorithm	Training set	1	10	100	S	M	L
Faster R-CNN	train	21.30	29.50	30.10	7.30	32.10	52.00
ION	train	23.20	32.70	33.50	10.10	37.70	53.60
Faster R-CNN	trainval35	23.80	34.00	34.60	12.00	38.50	54.40
SSD300	trainval35	22.50	33.20	35.30	9.60	37.60	56.50
SSD512	trainval35	24.80	37.50	39.80	14.00	43.50	59.00
YOLOV2	trainval35	20.70	31.60	33.30	9.80	36.50	54.40
Ours	trainval35	25.50	38.30	39.30	19.70	45.50	55.40

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Bing Zhou, Runxin Li, Zhenhong Shang, Xiaowu Li. Object Detection Algorithm Based on Improved Faster R-CNN[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101009

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