## 1 Introduction  
With the development of science and technology and the maturity of artificial intelligence technology, All industries are moving in the direction of intelligence. As one of the important pillar industries in China, construction machinery has played a vital role in the development of construction infrastructure in China [1]. Construction machinery covers infrastructure, mining, and other fields. The working environment is relatively bad, often accompanied by a high temperature, dust, vibration, etc. The complex working environment poses a serious threat to the safety of workers; part of the work of construction machinery is highly repetitive, and the working area is relatively fixed. Therefore, reducing the safety risk caused by construction machinery operation, saving labor cost, and developing unmanned and intelligent construction machinery have an important practical significance. According to the work requirements of construction machinery, this paper carried out research on technology for the intelligent identification of engineering materialsin complex environments, realized the intelligent identification of construction machinery and material categories by loaders, and made independent decisions according to the surrounding environment to improve work efficiency and ensure the work safety of staff. In recent years, with the development of computer hardware, target detection algorithms based on deep convolution neural network [2; 3] have developed rapidly and have been widely used in various industries. These kinds of algorithms can learn target features from a large number of images and have the characteristics of a high detection accuracy and a good robustness. It is an important research direction in the field of computer vision. In the process of material identification, the material features are not prominent and obvious, which directly affects the final identification accuracy. Therefore, to fully mine the details of the image to ensure the accuracy of recognition, it is necessary to enhance the contrast of low-contrast image to improve the quality of the image. After increasing its clarity, the image is combined with a convolution neural network to improve the accuracy of recognition. To date, many experts and scholars have proposed many contrast enhancement algorithms based on different theoretical foundations. Most of these algorithms can effectively enhance the contrast of the image, thereby improving the visual effect of the image [4]. For example, the wavelet-based homomorphic filter proposed by [PERSON] et al. [5], the S-function image contrast enhancement algorithm based on human eye brightness perception proposed by [PERSON] et al. [6], and the low-illumination image contrast enhancement algorithm based on the retinex theory proposed by [PERSON] et al. [7] have achieved good results in processing image contrast. This provides a good theoretical basis for the adaptive color scale enhancement algorithm proposed in this paper. This model mainly performs a color scale enhancement pretreatment on the collected data such as foggy days and dark environments, highlighting the target features in the scene, and providing a good basis for neural network training. The target detection algorithm based on convolutional neural network is mainly divided into a two-stage target detection algorithm and a one-stage target monitoring algorithm. A two-stage target detection algorithm first forms a target candidate frame and then detects the target through a convolution neural network. The detection accuracy is high, but the real-time performance is poor. Representative methods include R-CNN [8], Fast R-CNN [9], etc. A single-stage detection algorithm detects the target directly by a convolution neural network without generating the target candidate frame in advance. The detection accuracy of that method is lower than that of a two-stage algorithm, but the detection speed is faster. Representative methods include YOLO [10], SSD [11], etc.