Automated Classification Method for Crime Scene Sketches

Table 1. Composition parameters of Inception
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Table 1. Composition parameters of Inception
Structure conv1_1 conv2_1 conv2_2 conv3_1 conv3_2 conv4_2 Output
Inception1a 64 96 128 16 32 32 256
Inception1b 128 128 192 32 96 64 480
Inception2a 256 160 320 32 128 128 832
Inception2b 384 192 384 48 128 128 1024

Table 2. Composition of dataset

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Table 2. Composition of dataset

	Training dataset	Validation dataset	Test dataset
Crime dataset scene overview sketch	17101	2138	2137
Self-drawn location sketch	10005	1251	1250
Map-screenshot location sketch	24173	3022	3021
Photograph	21730	2716	2716
Total	73009	9127	9124

Table 3. Detection accuracy and parameters
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Table 3. Detection accuracy and parameters
Accuracy on testset /% Parameter quantity
AlexNet 94.87 56884612
XCTNet 98.65 4935460

Table 4. Confusion matrix

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Table 4. Confusion matrix

Confusion matrix		Real label				Accuracy /%
Confusion matrix			Crime sceneoverview sketch	Self-drawnlocation sketch	Map-screenshotlocation sketch	Negativeclass
	Crime sceneoverview sketch	2097	39	1	0	98.13
Predicted label	Self-drawnlocation sketch	67	1177	5	1	94.16
	Map-screenshotlocation sketch	3	3	3012	3	99.70
	Negative class	0	0	1	2715	99.96

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Kaixuan Wang, Zhuorong Li, Xiaobin Wang, Shengdong Yan, Yunqi Tang. Automated Classification Method for Crime Scene Sketches[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041009

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