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With the increasing popularity of electric vehicles, the number of end-of-life (EOF) electric vehicle batteries (EVBs) is also increasing day by day. Efficient dismantling and recycling of EVBs are essential to ensure environmental protection. There are many types of EVBs with complex structures, and the current automatic dismantling line is immature and lacks corresponding dismantling equipment. This makes it difficult for some small parts to be disassembled precisely. Screws are used extensively in batteries to fix or connect modules in EVBs. However, due to the small size of screws and differences in installation angles, screw detection is a very challenging task and a significant obstacle to automatic EVBs disassembly. This research proposes a systematic method to complete screw detection called “Active Screw Detection”. The experimental results show that with the YOLOX-s model, the improved YOLOX model achieves <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>95.92</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>92.14</mn><mo>%</mo></mrow></semantics></math></inline-formula> accuracy for both <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>50</mn></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>75</mn></msub></mrow></semantics></math></inline-formula> positioning after autonomous adjustment of the robotic arm attitude. Compared to the method without autonomous adjustment of the robotic arm, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>50</mn></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>75</mn></msub></mrow></semantics></math></inline-formula> improved by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>62.81</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>57.67</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively. In addition, the improved YOLOX model improves <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>50</mn></msub></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>m</mi><mi>A</mi><msub><mi>P</mi><mn>75</mn></msub></mrow></semantics></math></inline-formula> by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.19</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>3.59</mn><mo>%</mo></mrow></semantics></math></inline-formula>, respectively, compared to the original YOLOX model.