It　is　crucial　to　ensure　workers　wear　safety　helmets　when　working　at　a　workplace　with　a　high　risk　of　safety　accidents,　such　as　construction　sites　and　mine　tunnels.　Although　existing　methods　can　achieve　helmet　detection　in　images,　their　accuracy　and　speed　still　need　improvements　since　complex,　cluttered,　and　large-scale　scenes　of　real　workplaces　cause　server　occlusion,　illumination　change,　scale　variation,　and　perspective　distortion.　So,　a　new　safety　helmet-wearing　detection　method　based　on　deep　learning　is　proposed.　Firstly,　a　new　multi-scale　contextual　aggregation　module　is　proposed　to　aggregate　multi-scale　feature　information　globally　and　highlight　the　details　of　concerned　objects　in　the　backbone　part　of　the　deep　neural　network.　Secondly,　a　new　detection　block　combining　the　dilate　convolution　and　attention　mechanism　is　proposed　and　introduced　into　the　prediction　part.　This　block　can　effectively　extract　deep　features　while　retaining　information　on　fine-grained　details,　such　as　edges　and　small　objects.　Moreover,　some　newly　emerged　modules　are　incorporated　into　the　proposed　network　to　improve　safety　helmet-wearing　detection　performance　further.　Extensive　experiments　on　open　dataset　validate　the　proposed　method.　It　reaches　better　performance　on　helmet-wearing　detection　and　even　outperforms　the　state-of-the-art　method.　To　be　more　specific,　the　mAP　increases　by　3.4%,　and　the　speed　increases　from　17　to　33　fps　in　comparison　with　the　baseline,　You　Only　Look　Once　(YOLO)　version　5X,　and　the　mean　average　precision　increases　by　1.0%　and　the　speed　increases　by　7　fps　in　comparison　with　the　YOLO　version　7.　The　generalization　ability　and　portability　experiment　results　show　that　the　proposed　improvements　could　serve　as　a　springboard　for　deep　neural　network　design　to　improve　object　detection　performance　in　complex　scenarios.