Table 2.

Architecture details of the DilatedSkinNet

Block	Layer	Kernel size,	Atrous dilation	Output size
		Feature maps	Rate
Input layer	$192\times 256\times 3$	–	–	$192\times 256\times 3$
Block 1	Conv1	$3\times 3$, 8	1	$192\times 256\times 8$
Block 2	Conv2_1	$3\times 3$, 16	2	$192\times 256\times 16$
	Conv2_2	$3\times 3$, 16	4	$192\times 256\times 16$
Block 3	Conv3_1	$3\times 3$, 32	4	$192\times 256\times 32$
	Conv3_2	$1\times 1$, 16	6	$192\times 256\times 16$
	Conv3_3	$3\times 3$, 32	8	$192\times 256\times 32$
Block 4	Conv4_1	$3\times 3$, 64	8	$192\times 256\times 64$
	Conv4_2	$1\times 1$, 32	10	$192\times 256\times 32$
	Conv4_3	$3\times 3$, 64	10	$192\times 256\times 64$
	Conv4_4	$3\times 3$, 64	12	$192\times 256\times 64$
Block 5	Conv5_1	$3\times 3$, 128	12	$192\times 256\times 128$
	Conv5_2	$1\times 1$, 64	12	$192\times 256\times 64$
	Conv5_3	$3\times 3$, 128	14	$192\times 256\times 128$
	Conv5_4	$1\times 1$, 64	14	$192\times 256\times 64$
	Conv5_5	$3\times 3$, 128	14	$192\times 256\times 128$
	final_Conv	$1\times 1$, 2	–	$192\times 256\times 2$
	Softmax	–	–	$192\times 256\times 2$
OutPutMap	Pixel classification	Cross entropy	Loss function	$192\times 256\times 2$