Recognition of malposition and location of high-temperature forgings play a critical role in the realisation of robotised die forging, which is an ongoing trend in intelligent manufacturing. This study is aimed at the robotised die forging of the scraper beam of armoured face conveyor, which is the only transporting equipment used in the coal mine workface. Firstly, a novel process to recognise the malposition and location of high-temperature forgings using two monocular cameras, one placed horizontally and another vertically, is proposed. Secondly, a novel image preprocessing algorithm combining the algorithms of grey linear transformation, exponential transformation, and median filtering is proposed. After processing the high-temperature forging image using the proposed image preprocessing algorithm, the grey difference between the target region and background region of the processed image is highly increased. This is conducive to the subsequent image segmentation and contour extraction processes conducted in the forging region. Thirdly, after the comparison and analysis of the three commonly used image segmentation methods, including edge detection, threshold segmentation, and region growing methods, it is discovered that the region growing method is suitable for image segmentation of high-temperature forgings. Fourthly, a two-way modified and blob analysis based forging location algorithm is proposed to reduce the location error caused by the axial and radial asymmetric flash during the forging process. Finally, the proposed algorithms are validated by experiments and the location recognition error of the proposed location algorithm is only 0.86059 mm. This study provide technical support for the realisation of robotised die forging.