In　order　to　improve　the　detection　performance　of　DSOD　for　small　objects,　the　RFB_a　network　module　and　Atrous　convolution　were　introduced　into　DSOD.　Firstly,　the　improved　algorithm　inputs　the　feature　map　generated　by　the　second　transition　layer　of　the　DSOD　network　into　the　RFB_a　network　module,　and　extracts　the　features　with　different　receptive　fields　through　the　Atrous　convolution　with　different　sampling　steps　of　the　RFB_a　network　for　small　objects　detection.　Then,　in　order　to　improve　the　semantic　information　in　the　feature　map,　an　Atrous　convolution　layer　with　rate　size　of　6　was　added　after　the　second　transition　layer　without　pooling.　Finally,　an　IOG　penalty　term　was　added　to　the　loss　function　to　prevent　the　prediction　box　from　overlapping　when　predicting　objects　of　the　same　class,　which　may　leads　to　detection　missing　after　NMS　processing.　Experimental　results　show　that,　compared　with　the　original　DSOD　algorithm,　the　improved　algorithm　can　obtain　higher　detection　accuracy　and　better　detection　ability　of　small　objects,　and　meanwhile　reduce　the　hardware　requirements　on　the　training　network.　©　2019,　Editorial　Office　of　Semiconductor　Optoelectronics.　All　right　reserved.